Centrality catalogue

Mohammed Saqr and Sonsoles López-Pernas

library(cograph)
data(student_interactions)

Centrality measures quantify the position of a node in a network. The simplest, degree, counts the ties of a node. Other measures are built from the distances between nodes, the shortest paths that pass through a node, walks of all lengths, or the community structure of the network. Each measure describes a different aspect of position, and the choice between them depends on the process the network is assumed to carry.

This catalogue documents the 191 measures available through centrality(), organised by family. Each entry gives a short description, the formula as implemented, a guide to interpreting the scores, and an example. The examples use student_interactions, a data set of observed interactions between students included in the package. Tuning parameters are arguments of centrality() and are listed in the argument catalogue.

centrality(student_interactions)
#>    node degree_all strength_all closeness_all betweenness  eigenvector
#> 1    Ac         33          129    0.01754386   26.342857 1.000000e+00
#> 2    Ad         20           36    0.01754386   42.541520 1.096110e-01
#> 3    Fi         24           51    0.01666667   35.721634 1.789565e-01
#> 4    Ik         14           24    0.01666667   25.844874 1.551369e-02
#> 5    Vx         26           43    0.01960784   90.717124 7.238902e-02
#> 6    Rt         20           37    0.01785714   63.135739 1.159931e-01
#> 7    Km         11           16    0.01639344   18.175108 2.804725e-02
#> 8    Gj         19           31    0.01818182  114.599049 3.265786e-02
#> 9    Bd         12           18    0.01612903   21.769264 9.607736e-03
#> 10   Ce         10           13    0.01612903   16.648629 4.473504e-03
#> 11   Oq         14           20    0.01754386   34.151726 2.293068e-02
#> 12   Ya         13           19    0.01612903   18.216122 1.758656e-02
#> 13   Mo         12           17    0.01587302   38.264502 1.003629e-01
#> 14   Hj         12           19    0.01754386   85.816522 2.013125e-02
#> 15   Tv         10           13    0.01666667   25.916306 1.320877e-02
#> 16   Eg         10           12    0.01639344   22.335171 5.783916e-03
#> 17   Pr         11           18    0.01666667   23.974060 7.602231e-02
#> 18   Qs         15           19    0.01785714   76.910851 1.511764e-02
#> 19   Xz         14           18    0.01639344   22.280159 8.533484e-03
#> 20   Np          8            8    0.01666667   12.044048 1.549052e-02
#> 21   Dg         13           13    0.01886792   29.240901 6.260099e-03
#> 22   Hk         16           25    0.01818182   72.176441 1.201845e-01
#> 23   Wy         11           16    0.01639344   34.014358 1.054705e-03
#> 24   Jl         15           18    0.01818182   67.359085 5.009801e-02
#> 25   Fh         21           55    0.01818182   78.588877 2.817489e-01
#> 26   Zb          7            8    0.01538462    9.583333 5.429715e-05
#> 27   Eh          7           13    0.01428571   34.325000 1.163185e-03
#> 28   Be         14           16    0.01851852  105.250898 2.203252e-03
#> 29   Df          8           10    0.01562500   11.026190 4.800876e-06
#> 30   Cf         12           15    0.01724138  119.109163 1.243207e-02
#> 31   Su          6            9    0.01369863   33.154401 1.028472e-04
#> 32   Ln          7            8    0.01408451    5.749708 1.376854e-02
#> 33   Gi          3            4    0.01351351    0.000000 0.000000e+00
#> 34   Uw          4            7    0.01250000    0.000000 0.000000e+00
#>       pagerank
#> 1  0.285861728
#> 2  0.052985644
#> 3  0.077591140
#> 4  0.024836857
#> 5  0.057364714
#> 6  0.042552472
#> 7  0.016655998
#> 8  0.025444498
#> 9  0.014321668
#> 10 0.010679742
#> 11 0.016087378
#> 12 0.016588876
#> 13 0.031180263
#> 14 0.019051413
#> 15 0.012644206
#> 16 0.010425091
#> 17 0.022794289
#> 18 0.019784870
#> 19 0.013229134
#> 20 0.008466679
#> 21 0.010345027
#> 22 0.040383192
#> 23 0.009067435
#> 24 0.020529375
#> 25 0.070538086
#> 26 0.005635780
#> 27 0.010080122
#> 28 0.009378924
#> 29 0.004957518
#> 30 0.017877547
#> 31 0.005136500
#> 32 0.007628879
#> 33 0.005483193
#> 34 0.004411765

Pass type = "all" for all ordinary-cost measures, include = "costly" to add costly ones, measures = c(...) to pick a subset, digits = to round, and sort_by = to order the result.

How to read this catalogue

Every measure below is documented with four fields:

• Concept — what the measure captures, in words.
• Definition — the formula as cograph computes it. Where its normalisation differs from a textbook statement, the formula shown is the one cograph uses.
• Meaning — how to read a high (or low) value.
• Example — a runnable call.

Notation

Throughout, $G = (V, E)$ is a graph with $n = |V|$ nodes and adjacency matrix $A = (a_{ij})$; $w_{ij}$ are edge weights, $N(v)$ is the neighbour set of $v$, $k_v = |N(v)|$ its degree, and $s(v) = \sum_u w_{vu}$ its strength. We write $d(u, v)$ for the shortest-path distance, $\Delta$ for the graph diameter, and $\sigma_{st}$ (resp. $\sigma_{st}(v)$) for the number of shortest $s$–$t$ paths (resp. those passing through $v$). $L = D - A$ is the graph Laplacian and $\mathbf{1}$ the all-ones vector.

Catalogue index

Degree, strength and local connectivity

X-degree · Clustering Degree Algorithm · Extended Neighborhood Coreness · Malatya Centrality · Volume Centrality · Degree Centrality · Indegree Centrality · Outdegree Centrality · Strength Centrality · Instrength Centrality · Outstrength Centrality · Expected Centrality · Leverage Centrality · Lobby Centrality · H-Index Strength Centrality · Local H-Index Centrality · Semi-Local Centrality · ClusterRank Centrality · Collective Influence Centrality · MNC Centrality · DMNC Centrality · LAC Centrality · Gravity Centrality · Neighborhood Connectivity · Entropy Variation, Degree · Flow Coefficient · Local Entropy · Weighted h-index · Redundancy

Distance and closeness

Improved Global Structure Model · Global Structure Model · Hybrid Global Structure Model · Exogenous Centrality · Improved Closeness Centrality · Extended Gravity Centrality · Closeness Centrality · Incloseness Centrality · Outcloseness Centrality · Harmonic Centrality · Inharmonic Centrality · Outharmonic Centrality · Residual Closeness Centrality · Dangalchev Closeness Centrality · Generalized Closeness Centrality · Harary Centrality · Average Distance Centrality · Barycenter Centrality · Wiener Centrality · Lin Centrality · Decay Centrality · Radiality Centrality · Gil-Schmidt Centrality · Integration Centrality · Eccentricity Centrality · Ineccentricity Centrality · Outeccentricity Centrality · Closeness Vitality · Entropy Centrality · Centroid Centrality · Distance Entropy · Local Dimension · Local Information Dimensionality · Access Information · Hide Information · Local Dimension, Fixed Radius · Fuzzy Local Dimension · Local Volume Dimension · Heatmap Centrality · Geodesic k-path · k-path Census · Distance-weighted Fragmentation · Geodesic Power Closeness

Shortest-path brokerage and flow

Randomized shortest paths (RSP) betweenness · Relative-entropy integrated evaluation · DK-based gravity model · Mixed gravitational centrality · Extended mixed gravitational centrality · Localized bridging centrality · Extended local bridging centrality · Proximal betweenness · Betweenness Centrality · Stress Centrality · Load Centrality · Length-scaled Betweenness · Distance-decayed Betweenness · Ego Betweenness · Bottleneck Centrality · Bridging Centrality · Local bridging (legacy degree product) · Percolation Centrality · Flow Betweenness Centrality · Current-Flow Betweenness Centrality · Current-Flow Closeness Centrality · Entropy Variation, Betweenness

Spectral, walk and influence

Trust-PageRank · Iterative resource allocation (IRA) · Improved iterative resource allocation (IIRA) · Multi-characteristics gravity model · SpectralRank · ControlRank · Node and Neighbor Layer Information · Expected Force · Modified Expected Force · Bridging capital · LineRank · Random walk decay · Graph regularization centrality · Adaptive LeaderRank · Weighted LeaderRank · Node Resistance Curvature · Dynamics-Sensitive Centrality · Finite-Horizon Diffusion Centrality · Dynamical Importance · Eigenvector Centrality · PageRank Centrality · Authority Centrality · Hub Centrality · SALSA Centrality · LeaderRank Centrality · Alpha Centrality · Bonacich Power Centrality · Katz Centrality · Hubbell Centrality · Subgraph Centrality · Laplacian Centrality · Communicability Centrality · Communicability Betweenness Centrality · Random Walk Centrality · Markov Centrality · Immediate Effects Centrality (IEC) · Second-Order Centrality · Information Centrality · Nonbacktracking Centrality · Diffusion Degree · Infection Centrality · Edge Percolated Component · VoteRank Centrality · Expected Influence 1-Step · Expected Influence 2-Step · Spanning Tree Centrality · Shapley Value, Game 1 · Shapley Value, Game 2 · Shapley Value, Game 3 · Rumor Centrality · DegreeDiscountIC · SingleDiscount · NCVoteRank · WVoteRank · EnRenew · VoteRank++ · Node Contraction · Improved Node Contraction · Two-Way Random Walk Betweenness

Neighbourhood structure and cohesion

Degree and importance of lines (DIL) · Lhc index · Hybrid characteristic centrality (HCC) · Extended hybrid characteristic centrality (EHCC) · KED method · Local neighbor contribution (LNC) · Neighborhood (neighbor distance) centrality · Coleman-Theil hierarchy · Maximal Clique Centrality · Node Truss Number · Mixed Degree Decomposition · Bridging Coefficient · Godfather Index · Supported Relationships · Transitivity Centrality · Constraint Centrality · Effective Size Centrality · Topological Coefficient Centrality · Diversity Centrality · Cross-Clique Centrality · Coreness Centrality · Onion Centrality · K-Reach Centrality · s-shell Index · Weighted k-shell · Renewed Coreness · s-core Index · Local Efficiency

Directed prestige and hierarchy

BG-index (beta power) · Prestige Domain Centrality · Prestige Domain Proximity Centrality · Local Reaching Centrality · Pairwise Disconnectivity Centrality · Trophic Level Centrality

Community and group-based

Map equation centrality · Participation Coefficient · Within-Module Z Centrality · Gateway Centrality · Brokerage Coordinator Centrality · Brokerage Itinerant Centrality · Brokerage Representative Centrality · Brokerage Gatekeeper Centrality · Brokerage Liaison Centrality · Modularity Vitality · Community Hub-Bridge · Community-Based Centrality · Comm Centrality · Community-Based Mediator

Degree, strength and local connectivity

X-degree

Nonbacktracking four-edge walks centered at each node.

$$Xdeg(i)=\left[\sum_{j\in N(i)}(d_j-1)\right]^2-\sum_{j\in N(i)}(d_j-1)^2$$

Meaning. A high value indicates a node at the centre of many non-backtracking four-step walks, a sign that its neighbours connect it to the wider network. Isolates, leaves and every node of a star score zero.

centrality_x_degree(student_interactions)

Clustering Degree Algorithm

Degree and strength adjusted by Barrat weighted clustering, plus weighted contributions from immediate neighbors.

$$PC_i=CD_i+\sum_j\frac{w_{ij}}{w_{\max}}CD_j,\quad CD_i=\frac{\alpha d_i+(1-\alpha)s_i}{1+e^{-C_i^w}}$$

Meaning. A high value indicates a node that is well connected and clustered, with neighbours that are too. cda_alpha (default 0.5) balances degree against strength. Because weights enter directly, their units matter.

centrality_cda(student_interactions)

Extended Neighborhood Coreness

The sum of neighbors’ neighborhood-coreness scores, using core numbers from the original simple undirected graph.

$$C_{nc+}(i)=\sum_{j\in N(i)}\sum_{l\in N(j)}k_s(l)=(A^2 k_s)_i$$

Meaning. Rewards access to core-rich neighborhoods. Every length-two walk contributes, including returns to the focal node and multiple walks reaching the same endpoint. Isolates score zero; on a regular graph of degree d it equals d cubed. Other inputs are projected to the simple undirected skeleton.

centrality_extended_coreness(student_interactions)

Malatya Centrality

The sum of a node’s degree divided by each neighbour’s degree, calculated on the original simple undirected graph.

$$M(i)=\sum_{j\in N(i)}\frac{d_i}{d_j}$$

Meaning. Favours nodes with many low-degree neighbours. Isolates score zero. On nonisolated vertices the score is exactly the reciprocal of the bridging coefficient; on a regular graph it equals degree.

centrality_malatya(student_interactions)

Volume Centrality

The total original-graph degree inside a closed hop neighbourhood, including its centre.

$$V_r(i) = \sum_{j:\,d(i,j)\leq r,\ d(i,j)<\infty} k_j$$

Meaning. Measures connectivity within and leaving the neighbourhood. Radius 0 gives degree, default 2 uses two hops, and infinite radius gives twice the component’s edge count. Uses the simple undirected skeleton.

centrality_volume(student_interactions)

Degree Centrality

Degree centrality counts the number of direct ties incident on a node. In directed graphs, it can be separated into incoming and outgoing ties.

$$C_D(v) = \sum_{u \in V} a_{vu} = k_v$$

Meaning. A high degree value indicates many direct observed connections. It is a local measure of connectivity.

centrality_degree(student_interactions)

Indegree Centrality

Indegree counts ties pointing into a node under a directed interpretation.

$$k_v^{\mathrm{in}} = \sum_{u \in V} a_{uv}$$

Meaning. A high indegree value indicates many incoming observed ties. Its substantive meaning depends on what edge direction represents.

centrality_indegree(student_interactions)

Outdegree Centrality

Outdegree counts ties leaving a node under a directed interpretation.

$$k_v^{\mathrm{out}} = \sum_{u \in V} a_{vu}$$

Meaning. A high outdegree value indicates many outgoing observed ties. In transition networks, this can mean many possible next states.

centrality_outdegree(student_interactions)

Strength Centrality

Strength centrality is weighted degree: it sums edge weights instead of counting edges.

$$s(v) = \sum_{u \in V} w_{vu}$$

Meaning. A high strength value indicates high total incident edge weight. This should be interpreted according to how weights were defined.

centrality_strength(student_interactions)

Instrength Centrality

Instrength sums incoming edge weights.

$$s^{\mathrm{in}}(v) = \sum_{u \in V} w_{uv}$$

Meaning. A high instrength value indicates high total incoming weight. It is a weighted incoming-connectivity measure.

centrality_instrength(student_interactions)

Outstrength Centrality

Outstrength sums outgoing edge weights.

$$s^{\mathrm{out}}(v) = \sum_{u \in V} w_{vu}$$

Meaning. A high outstrength value indicates high total outgoing weight. Diversity describes how that weight is spread across ties.

centrality_outstrength(student_interactions)

Expected Centrality

Expected centrality sums the degrees of a node’s neighbours.

$$C_{\mathrm{exp}}(v) = \sum_{u \in N(v)} k_u$$

Meaning. A high value indicates adjacency to well-connected nodes.

centrality_expected(student_interactions)

Leverage Centrality

Leverage centrality compares a node’s degree with the degrees of its neighbours.

$$\ell(v) = \frac{1}{k_v} \sum_{u \in N(v)} \frac{k_v - k_u}{k_v + k_u}$$

Meaning. A positive high value indicates that the node is more connected than its neighbours under this degree comparison.

centrality_leverage(student_interactions)

Lobby Centrality

Lobby centrality is the h-index of neighbour degrees.

$$h(v) = \max\bigl\{ h : |\{ u \in \{v\} \cup N(v) : k_u \ge h \}| \ge h \bigr\}$$

Meaning. A high value indicates many neighbours that themselves have reasonably high degree.

centrality_lobby(student_interactions)

H-Index Strength Centrality

H-index strength is a weighted h-index-style neighbourhood measure.

$$h_s(v) = \max\bigl\{ h : |\{ u \in \{v\} \cup N(v) : s(u) \ge h \}| \ge h \bigr\}$$

Meaning. A high value indicates strong ties to neighbours meeting a weighted connectivity threshold.

centrality(student_interactions, measures = "hindex_strength")

Local H-Index Centrality

Local h-index centrality iteratively applies h-index logic to local neighbourhoods.

$$h^{(t+1)}(v) = \mathcal{H}\bigl(\{ h^{(t)}(u) : u \in N(v) \}\bigr), \quad h^{(0)}(v) = k_v$$

Meaning. A high value indicates a locally robust neighbourhood position under the h-index updating rule.

centrality(student_interactions, measures = "local_hindex")

Semi-Local Centrality

Semi-local centrality extends degree-like counting beyond immediate neighbours.

$$C_{SL}(v) = \sum_{u \in N(v)} \sum_{w \in N(u)} \bigl| \{\, x : d(w, x) \le 2 \,\} \bigr|$$

Meaning. A high value indicates proximity to a locally well-connected neighbourhood.

centrality_semilocal(student_interactions)

ClusterRank Centrality

ClusterRank combines neighbour degree information with local clustering.

$$C_{CR}(v) = c(v) \sum_{u \in N(v)} \bigl( k_u + 1 \bigr), \quad c(v) = \text{local clustering coefficient}$$

Meaning. A high value indicates local connectedness adjusted for clustering-related redundancy.

centrality_clusterrank(student_interactions)

Collective Influence Centrality

Collective influence combines a node’s excess degree with excess degree on a local boundary.

$$\mathrm{CI}_\ell(v) = (k_v - 1) \sum_{u \,\in\, \partial B(v, \ell)} (k_u - 1), \quad \ell = 2$$

Meaning. A high value indicates potential importance under the collective influence model.

centrality(student_interactions, measures = "collective_influence")

MNC Centrality

Maximum neighbourhood component centrality is the size of the largest connected component in a node’s neighbourhood.

$$\mathrm{MNC}(v) = \max_{C \,\in\, \mathcal{C}(G[N(v)])} |C|$$

Meaning. A high value indicates that many neighbours belong to one connected local component.

centrality_mnc(student_interactions)

DMNC Centrality

DMNC is a density-adjusted version of maximum neighbourhood component centrality.

$$\mathrm{DMNC}(v) = \frac{E_c}{N_c^{\,\varepsilon}}, \quad \varepsilon = 1.7$$

Meaning. A high value indicates a large and dense local neighbour component under the chosen density exponent. Here $E_c$ and $N_c$ are the edges and nodes of the largest component of $G[N(v)]$.

centrality_dmnc(student_interactions)

LAC Centrality

LAC, or local average connectivity, measures connectivity in a node’s neighbourhood.

$$\mathrm{LAC}(v) = \frac{1}{k_v} \sum_{u \in N(v)} \deg_{G[N(v)]}(u)$$

Meaning. A high value indicates that the node’s neighbours form a relatively connected local structure.

centrality_lac(student_interactions)

Gravity Centrality

Gravity centrality treats each node’s mass as attracting the mass of others over graph distance, and can be truncated at a radius.

$$G(v) = \sum_{u \ne v,\; d(u,v) \le R} \frac{m_v\, m_u}{d(u, v)^2}, \quad m = \text{k-shell or degree}$$

Meaning. A high value indicates proximity to massive nodes. By default mass is the k-shell and only nodes within three steps contribute; gravity_mass = "degree", gravity_radius = NULL gives the degree-based gravity model over all reachable nodes, and gravity_radius = "auto" sets the radius from the network.

centrality_gravity(student_interactions)

Neighborhood Connectivity

Neighborhood connectivity is the mean degree of a node’s neighbours (average neighbour degree).

$$C_{NC}(v) = \frac{1}{k_v} \sum_{u \in N(v)} k_u$$

Meaning. A high value indicates attachment to hubs. Isolates score 0. Direction is respected under mode.

centrality_neighborhood_connectivity(student_interactions)

Entropy Variation, Degree

Entropy variation is the drop in the Shannon entropy of the degree distribution when the node and its links are removed.

$$EnV_k(i) = I_k(G) - I_k(G - i), \quad I_k(G) = -\sum_j \frac{k_j}{\sum_l k_l} \ln \frac{k_j}{\sum_l k_l}$$

Meaning. A high value indicates a node whose removal makes the remaining degree distribution more concentrated. Signed; negative values occur.

centrality(student_interactions, measures = "entropy_variation_degree")

Flow Coefficient

The flow coefficient is the share of a node’s neighbour pairs that are connected only through the node.

$$fc(v) = \frac{|\{(j, k) : j \to v \to k,\ j \not\to k\}|}{k_v (k_v - 1)}$$

Meaning. A high value indicates a node that mediates local flow. On an undirected graph it equals one minus the clustering coefficient.

centrality_flow_coefficient(student_interactions)

Local Entropy

Local entropy sums $-k \log k$ over a node’s neighbours.

$$LE(v) = -\sum_{u \in N(v)} k_u \ln k_u$$

Meaning. Always non-positive; a lower (more negative) value indicates a larger, denser neighbourhood. Isolates score 0.

centrality_local_entropy(student_interactions)

Weighted h-index

The weighted h-index takes the h-index over topological link weights, each neighbour’s weight repeated by its degree.

$$h^w_v = H\big(\{k_v k_u \text{ repeated } k_u \text{ times} : u \in N(v)\}\big)$$

Meaning. A high value indicates a node with many well-connected neighbours. Input edge weights are ignored.

centrality_weighted_h_index(student_interactions)

Redundancy

Redundancy is the mean degree of a node’s neighbours inside its ego network.

$$r(v) = \frac{2 t_v}{k_v} = k_v - \text{effective size}(v)$$

Meaning. A high value indicates neighbours that are connected to each other, so fewer structural holes.

centrality_redundancy(student_interactions)

Distance and closeness

Improved Global Structure Model

Focal degree influence combined with degrees of reachable partners and a distance exponent set by global mean degree.

$$IGSM_i=e^{k_i/N}\sum_{j\ne i}\frac{k_j}{d_{ij}^{a}},\quad a=\lceil\log_2\overline{k}\rceil$$

Meaning. A high value indicates a high-degree node lying close to other high-degree nodes. The distance penalty is set from the network’s mean degree, so it is steeper in denser networks. Only reachable nodes contribute.

centrality_improved_global_structure(student_interactions)

Global Structure Model

Focal coreness combined with distance-discounted coreness of all reachable partners.

$$GSM_i=e^{k_s(i)/N}\sum_{j\ne i}\frac{k_s(j)}{d_{ij}}$$

Meaning. A high value indicates a node in the network’s core that lies close to many other core nodes. Every reachable node contributes, discounted by distance. Isolates score zero.

centrality_global_structure(student_interactions)

Hybrid Global Structure Model

Exponential degree-coreness influences with a distance penalty set by their global mean.

$$s_i=e^{k_s(i)k_i/N},\quad a=\lceil\log_2\overline{s}\rceil,\quad H\!GSM_i=s_i\sum_{j\ne i}\frac{s_j}{d_{ij}^{a}}$$

Meaning. A high value indicates a node that combines high degree and coreness and lies close to other such nodes. The distance penalty is set from the network’s average of these combined masses. Only reachable nodes contribute.

centrality_hybrid_global_structure(student_interactions)

Exogenous Centrality

Contribution of a node to the base centrality of all other nodes, measured by deleting it.

$$E_i=\sum_{j\ne i}\left[C_G(j)-C_{G-i}(j)\right]$$

Meaning. A high value indicates a node that contributes much to the centrality of others: removing it lowers their centrality most. The base measure is set with exogenous_base (reverse closeness by default, or degree or betweenness). With betweenness, removing a node can raise other scores, so values can be negative.

centrality_exogenous(student_interactions)

Improved Closeness Centrality

Closeness adjusted for the number of shortest paths connecting each pair of nodes.

$$ICC_i=\frac{n-1}{\sum_{j\ne i}d_{ij}/\sigma_{ij}^{\alpha}},\quad 0\le\alpha\le1$$

Meaning. A high value indicates a node that is close to others and joined to them by many shortest paths. icc_alpha (default 0.2) sets how much multiple shortest paths shorten the effective distance; at 0 the measure is ordinary closeness. Disconnected graphs score zero at every node.

centrality_improved_closeness(student_interactions)

Extended Gravity Centrality

The sum of immediate neighbors’ raw gravity scores, with k-shell masses and hop distances.

$$G^+(i)=\sum_{j\in N(i)}\sum_{l:0<d(j,l)\le r}\frac{k_s(j)k_s(l)}{d(j,l)^2}$$

Meaning. A high value indicates a node whose neighbours have high gravity centrality. Because the radius (gravity_radius, default 3) is measured from each neighbour, contributions can come from one step beyond it.

centrality_extended_gravity(student_interactions)

Closeness Centrality

Closeness centrality summarises how short a node’s paths are to other reachable nodes.

$$C_C(v) = \frac{1}{\sum_{u \ne v} d(v, u)}$$

Meaning. A high value indicates short graph distances to other nodes. In disconnected graphs, harmonic centrality may be easier to interpret.

centrality_closeness(student_interactions)

Incloseness Centrality

Incloseness computes closeness over incoming directed paths.

$$C_C^{\mathrm{in}}(v) = \frac{1}{\sum_{u \ne v} d(u, v)}$$

Meaning. A high value indicates that other nodes can reach the focal node through short directed paths.

centrality_incloseness(student_interactions)

Outcloseness Centrality

Outcloseness computes closeness over outgoing directed paths.

$$C_C^{\mathrm{out}}(v) = \frac{1}{\sum_{u \ne v} d(v, u)}$$

Meaning. A high value indicates that the focal node can reach other nodes through short directed paths.

centrality_outcloseness(student_interactions)

Harmonic Centrality

Harmonic centrality sums reciprocal shortest-path distances. Unreachable nodes contribute zero.

$$C_H(v) = \sum_{u \ne v} \frac{1}{d(v, u)}$$

Meaning. A high value indicates broad reach through short paths while handling disconnected components more gracefully than classical closeness.

centrality_harmonic(student_interactions)

Inharmonic Centrality

Inharmonic centrality computes harmonic centrality over incoming directed paths.

$$C_H^{\mathrm{in}}(v) = \sum_{u \ne v} \frac{1}{d(u, v)}$$

Meaning. A high value indicates that the node is reachable from many others through short directed paths.

centrality_inharmonic(student_interactions)

Outharmonic Centrality

Outharmonic centrality computes harmonic centrality over outgoing directed paths.

$$C_H^{\mathrm{out}}(v) = \sum_{u \ne v} \frac{1}{d(v, u)}$$

Meaning. A high value indicates that many nodes can be reached from the focal node through short directed paths.

centrality_outharmonic(student_interactions)

Residual Closeness Centrality

Residual closeness sums $1 / 2^d$ over shortest-path distances (the focal node contributes $1$).

$$C_R(v) = \sum_{u \in V} 2^{-d(v, u)}$$

Meaning. A high value indicates many close reachable nodes with rapid distance decay.

centrality_residual_closeness(student_interactions)

Dangalchev Closeness Centrality

Dangalchev closeness is the residual-closeness distance-decay measure.

$$C_D(v) = \sum_{u \in V} 2^{-d(v, u)}$$

Meaning. A high value indicates short-distance reach with distant nodes down-weighted.

centrality_dangalchev(student_interactions)

Generalized Closeness Centrality

Generalized closeness sums $\\alpha^d$, where $d$ is the shortest-path distance.

$$C_G(v) = \sum_{u \in V} \alpha^{\,d(v, u)}, \quad \alpha = 0.5$$

Meaning. A high value indicates reach under the chosen distance-decay parameter.

centrality_generalized_closeness(student_interactions)

Harary Centrality

Harary centrality sums inverse squared shortest-path distances.

$$C_{\mathrm{Har}}(v) = \sum_{u \ne v} \frac{1}{d(v, u)^2}$$

Meaning. A high value indicates many nearby reachable nodes, with distant nodes strongly down-weighted.

centrality_harary(student_interactions)

Average Distance Centrality

Average distance centrality summarises mean shortest-path distance from a node (the focal node’s $d = 0$ term is included; the centiserve convention divides by $n + 1$).

$$\bar{d}(v) = \frac{\sum_{u \in V} d(v, u)}{n + 1}$$

Meaning. Lower values indicate shorter average graph distance. The direction of interpretation should be checked because it is a distance quantity.

centrality_average_distance(student_interactions)

Barycenter Centrality

Barycenter centrality is the inverse of the sum of shortest-path distances.

$$C_{\mathrm{Bary}}(v) = \frac{1}{\sum_{u \ne v} d(v, u)}$$

Meaning. A high value indicates a small total distance to other reachable nodes.

centrality_barycenter(student_interactions)

Wiener Centrality

Wiener centrality records total shortest-path distance from a node (unreachable pairs contribute zero).

$$W(v) = \sum_{u \ne v} d(v, u)$$

Meaning. Lower values indicate shorter total distance. It is better read as distance burden than influence.

centrality_wiener(student_interactions)

Lin Centrality

Lin centrality combines reachable nodes and total distance to those nodes.

$$C_{\mathrm{Lin}}(v) = \frac{|R(v)|^2}{\sum_{u \in R(v)} d(v, u)}, \quad R(v) = \{ u \ne v : d(v,u) < \infty \}$$

Meaning. A high value indicates many reachable nodes through relatively short paths.

centrality_lin(student_interactions)

Decay Centrality

Decay centrality sums reach discounted by distance (the focal node contributes $1$).

$$C_{\delta}(v) = \sum_{u \in V} \delta^{\,d(v, u)}, \quad \delta = 0.5$$

Meaning. A high value indicates access to many nodes, especially nearby nodes. The value depends on the decay parameter.

centrality_decay(student_interactions)

Radiality Centrality

Radiality compares node distances to the graph diameter.

$$\mathrm{Rad}(v) = \frac{\sum_{u \ne v} \bigl( \Delta + 1 - d(v, u) \bigr)}{n - 1}$$

Meaning. A high value indicates short distances to others relative to overall graph diameter.

centrality_radiality(student_interactions)

Gil-Schmidt Centrality

Gil-Schmidt centrality sums reciprocal distances and normalises by graph size.

$$C_{GS}(v) = \frac{1}{n - 1} \sum_{u \ne v} \frac{1}{d(v, u)}$$

Meaning. A high value indicates broad reciprocal-distance access to the network.

centrality_gilschmidt(student_interactions)

Integration Centrality

Integration centrality is a distance-based measure of how integrated a node is within the graph.

$$\mathrm{Int}(v) = \sum_{u \ne v} \left( 1 - \frac{d(v, u) - 1}{d_{\max}} \right), \quad d(v,u) = d_{\max} + 1 \text{ if unreachable}$$

Meaning. A high value indicates broad distance-based access to the network.

centrality_integration(student_interactions)

Eccentricity Centrality

Eccentricity is the maximum shortest-path distance from a node to any reachable node.

$$\varepsilon(v) = \max_{u \in V} d(v, u)$$

Meaning. Lower eccentricity generally indicates smaller worst-case distance. A high value means at least one reachable node is far away.

centrality_eccentricity(student_interactions)

Ineccentricity Centrality

Ineccentricity computes eccentricity over incoming directed paths.

$$\varepsilon^{\mathrm{in}}(v) = \max_{u \in V} d(u, v)$$

Meaning. It summarises the largest directed distance from other nodes into the focal node.

centrality_ineccentricity(student_interactions)

Outeccentricity Centrality

Outeccentricity computes eccentricity over outgoing directed paths.

$$\varepsilon^{\mathrm{out}}(v) = \max_{u \in V} d(v, u)$$

Meaning. It summarises the largest directed distance from the focal node to reachable others.

centrality_outeccentricity(student_interactions)

Closeness Vitality

Closeness vitality measures how total network distance changes when a node is removed, using the Wiener index $W(G) = \\sum_{s, t} d(s, t)$.

$$\mathrm{CV}(v) = W(G) - W(G \setminus v)$$

Meaning. A high value indicates that removing the node substantially changes shortest-path structure.

centrality_closeness_vitality(student_interactions)

Entropy Centrality

Entropy centrality measures the change in graph entropy associated with a node, from the distribution of finite shortest-path distances after the node is removed.

$$H(v) = -\sum_{w} Y_w \log_2 Y_w, \quad Y_w = \frac{|\{ x : d(w, x) < \infty \}|}{\sum_{w'} |\{ x : d(w', x) < \infty \}|}$$

Meaning. A high value indicates strong contribution under the graph entropy definition being used.

centrality_entropy(student_interactions)

Centroid Centrality

Centroid centrality compares distance dominance between pairs of nodes.

$$f(v, u) = \gamma(v, u) - \gamma(u, v), \quad \gamma(v, u) = |\{ w : d(v, w) < d(u, w) \}|, \quad C_{\mathrm{cen}}(v) = \min_{u \ne v} f(v, u)$$

Meaning. A high value indicates a favourable position in pairwise distance comparisons.

centrality_centroid(student_interactions)

Distance Entropy

Distance entropy is the normalised Shannon entropy of a node’s hop-distance profile, so it summarises the spread of distances where closeness summarises their mean.

$$h(v) = -\frac{1}{\log(M_v - m_v + 1)} \sum_{k = m_v}^{M_v} p_k \log p_k, \quad p_k = \frac{n_k(v)}{R_v}$$

Meaning. A high value (up to 1) indicates reach spread evenly across many network layers; 0 means every reachable node sits at the same distance. Hop counts only; weights are ignored.

centrality_distance_entropy(student_interactions)

Local Dimension

Local dimension is the growth exponent of the ball around a node: how fast the number of nodes within $r$ hops grows with $r$.

$$D_v = \frac{d \ln B_v(r)}{d \ln r}, \quad B_v(r) = 1 + |\{u : d(v, u) \le r\}|, \; r = 1, \ldots, d_{\max}(v)$$

Meaning. A low value indicates a node that reaches most of the network within a few hops, so lower is more influential. With a single radius the discretised derivative $r\, n_v(r) / B_v(r)$ is reported.

centrality_local_dimension(student_interactions)

Local Information Dimensionality

Local information dimensionality replaces the ball count of local dimension by its Shannon information and grows the box only to half the node’s eccentricity.

$$D^I_v = -\frac{d I_v(l)}{d \ln l}, \quad I_v(l) = -p_v(l) \ln p_v(l), \; p_v(l) = \frac{B_v(l)}{n}, \; l = 1, \ldots, \lceil d_{\max}(v) / 2 \rceil$$

Meaning. A high value indicates a more influential node. With a single box size the discretised derivative $l (1 + \ln p_v(l))\, n_v(l) / n$ is reported.

centrality_local_information_dimension(student_interactions)

Access Information

Access information is the mean number of bits a map-less walker needs to reach every other node along shortest paths.

$$A_i = \frac{1}{N} \sum_j S(i \to j), \quad S(i \to j) = -\log_2 \sum_{p(i, j)} \frac{1}{k_i} \prod_{l \in p,\, l \ne i, j} \frac{1}{k_l - 1}$$

Meaning. A low value indicates a node that reaches the network with few decisions. Hubs score high, because a walker leaving a hub has many links to choose from.

centrality_access_information(student_interactions)

Hide Information

Hide information is the mean number of bits the rest of the network needs to locate a node.

$$H_i = \frac{1}{N} \sum_j S(j \to i)$$

Meaning. A high value indicates a hidden, peripheral node; hubs have low hide information.

centrality_hide_information(student_interactions)

Local Dimension, Fixed Radius

The Silva-Costa local dimension is the discretised growth exponent of the ball around a node at one chosen radius.

$$D_v(r) = \frac{r\, n_v(r)}{B_v(r)}$$

Meaning. A structural descriptor: higher values mean the neighbourhood is still growing fast at that radius. Nodes with eccentricity below the radius score 0.

centrality_local_dimension_fixed(student_interactions)

Fuzzy Local Dimension

Fuzzy local dimension replaces the ball count by a Gaussian-weighted average and takes its log-log slope.

$$N_v(r) = \frac{\sum_{d_{vu} \le r} e^{-d_{vu}^2 / r^2}}{|\{u : d_{vu} \le r\}|}, \quad FLD(v) = \frac{d \log N_v(r)}{d \log r}$$

Meaning. A high value indicates a more influential node (the opposite orientation to the rest of the dimension family).

centrality_fuzzy_local_dimension(student_interactions)

Local Volume Dimension

Local volume dimension is the log-log slope of the total degree inside the ball around a node.

$$V_v(l) = \sum_{d_{vu} \le l} k_u, \quad LVD(v) = \frac{d \ln V_v(l)}{d \ln l}$$

Meaning. A low value indicates a more important node. The source article is closed access; the definition follows the authors’ later preprint.

centrality_local_volume_dimension(student_interactions)

Heatmap Centrality

Heatmap centrality is a node’s farness minus the mean farness of its neighbours.

$$C_{HM}(v) = f(v) - \frac{1}{k_v} \sum_{u \in N(v)} f(u)$$

Meaning. A low (more negative) value indicates a more central node. Isolates are undefined.

centrality_heatmap(student_interactions)

Geodesic k-path

Geodesic k-path centrality counts the shortest paths of length at most $k$ that start at a node, with multiplicity.

$$C_k(v) = \sum_{0 < d(v, u) \le k} \sigma(v, u)$$

Meaning. A high value indicates many short geodesics leaving the node. Counting nodes instead of paths gives m-reach, which is what centiserve::geokpath computes.

centrality_geodesic_kpath(student_interactions)

k-path Census

The k-path census counts the simple paths of length at most $k$ that a node lies on, endpoints included.

$$C_{kP}(v) = |\{\, P : |P| \le k,\; v \in P \,\}|$$

Meaning. A high value indicates a node embedded in many short walks. Length 1 alone reproduces degree. Enumeration is exhaustive, so cost grows with the branching factor to the power $k$.

centrality_kpath(student_interactions)

Distance-weighted Fragmentation

Distance-weighted fragmentation asks how much worse the network communicates once a node is deleted.

$$F_d(v) = 1 - \frac{\sum_{i \ne j \ne v} 1 / d_{ij}^{\,G - v}}{(n-1)(n-2)}$$

Meaning. A high value indicates a node whose removal fragments the network or stretches its distances. A node inside a clique scores near 0.

centrality_fragmentation(student_interactions)

Geodesic Power Closeness

Geodesic power closeness raises every distance to a negative power, so one exponent moves the measure between a local and a global reading.

$$c_\delta(i) = \frac{1}{n-1} \sum_{j \ne i} d_{ij}^{-\delta}$$

Meaning. A high value indicates a node close to many others. The exponent spans the family: $\delta = 1$ is harmonic centrality over $n-1$, $\delta = 2$ is the inverse-square sum, a large $\delta$ approaches degree, and $\delta = 0$ counts the reachable set. Unreachable nodes contribute nothing but stay in the denominator.

centrality_delta_closeness(student_interactions)

Shortest-path brokerage and flow

Randomized shortest paths (RSP) betweenness

Count the visits a node gets from walks that lie between shortest paths and pure random walks, with one parameter setting the balance.

$$bet_i=\sum_{s=1}^{n}\sum_{t=1}^{n}\Bigl(\frac{z_{si}}{z_{st}}-\frac{z_{ti}}{z_{tt}}\Bigr)z_{it},\qquad \mathbf{Z}=(\mathbf{I}-\mathbf{W})^{-1},\quad \mathbf{W}=(\mathbf{D}^{-1}\mathbf{A})\circ\exp(-\beta\mathbf{C})$$

Meaning. A high value indicates a node that walks between other pairs of nodes pass through often. rsp_beta moves the measure between a random walk (small values; the default 0.01 lies near this end) and shortest-path betweenness (large values). rsp_cost sets whether a weight is read as an affinity or as a distance.

centrality_rsp_betweenness(student_interactions)

Relative-entropy integrated evaluation

Turn several indexes into distributions and take the distribution closest to all of them.

$$u_{ji}=C_j(i)/\textstyle\sum_k C_j(k)\ \text{or}\ (1-C_j(i)/\sum_k C_j(k))/\sum_l(1-C_j(l)/\sum_k C_j(k)),\quad w_i=\prod_{j=1}^{m}u_{ji}^{1/m}\Big/\sum_{i}\prod_{j=1}^{m}u_{ji}^{1/m}$$

Meaning. A high value indicates a node that ranks highly on several centrality indexes at once. The score is the normalised geometric mean of the indexes chosen with re_indexes, and it sums to one over the nodes. A node that scores zero on any one index scores zero overall.

centrality_relative_entropy(student_interactions)

DK-based gravity model

Degree, k-shell and the stage at which peeling reached the node act together as gravitational mass.

$$k_s^*(i)=k_s(i)+p(i)/(\max_k q(k)+1),\quad DK(i)=k(i)+k_s^*(i),\quad DKGM_i=\sum_{j\ne i,\,d(i,j)\le R}DK(i)DK(j)/d(i,j)^2$$

Meaning. A high value indicates a node of large mass lying close to other nodes of large mass, where mass combines degree, k-shell and how late in the peeling the node was removed. Only nodes within dkgm_radius steps (default 2) contribute. The score depends on the whole graph, so adding a component can change it.

centrality_dkgm(student_interactions)

Mixed gravitational centrality

Core-number mass at the source interacts with degree mass at nearby nodes.

$$MGC_i=k_s(i)\sum_{j:0<d(i,j)\le r}k(j)/d(i,j)^2$$

Meaning. A high value indicates a node deep in the network’s core that lies close to many high-degree nodes: the node’s own mass is its coreness and its partners’ mass is their degree. Only nodes within gravity_radius steps (default 3) contribute; a radius of 1 restricts the sum to neighbours.

centrality_mixed_gravity(student_interactions)

Extended mixed gravitational centrality

Sum immediate neighbors’ raw mixed gravitational scores.

$$EMGC_i=\sum_{j\in N(i)}k_s(j)\sum_{l:0<d(j,l)\le r}k(l)/d(j,l)^2$$

Meaning. A high value indicates a node whose neighbours have high mixed gravitational centrality. Because the radius is measured from each neighbour, contributions can come from up to gravity_radius + 1 steps away (default 3).

centrality_extended_mixed_gravity(student_interactions)

Localized bridging centrality

Brokerage in the one-hop ego network, adjusted for neighbor degrees.

$$LBC(v)=B_{G[N_{\leq1}(v)]}(v)\,\frac{1/d_v}{\sum_{u\in N(v)}1/d_u}$$

Meaning. A high value indicates a node that brokers among its immediate neighbours and has few ties itself while its neighbours have many. The score is betweenness within the node’s one-step neighbourhood, multiplied by the bridging coefficient. Isolates and leaves score zero.

centrality_localized_bridging(student_interactions)

Extended local bridging centrality

Brokerage in the two-hop ego network, adjusted for neighbor degrees.

$$LBC_2(v)=B_{G[N_{\leq2}(v)]}(v)\,\frac{1/d_v}{\sum_{u\in N(v)}1/d_u}$$

Meaning. A high value indicates a node that brokers within its two-step neighbourhood and has few ties itself while its neighbours have many. The score is betweenness within the subgraph of nodes up to two steps away, multiplied by the bridging coefficient.

centrality_extended_local_bridging(student_interactions)

Proximal betweenness

Brokerage at the first or last intermediate vertex of shortest paths.

$$C_{ps}(v)=\sum_{s,t:(v,t)\in E}\sigma_{st}(v)/\sigma_{st}$$

Meaning. A high value indicates a node that often lies on shortest paths directly next to one of the endpoints. By default a node is counted as the last intermediary before the destination; proximal_variant selects the first after the origin, or both.

centrality_proximal_betweenness(student_interactions)

Betweenness Centrality

Betweenness centrality measures how often a node lies on shortest paths between other pairs of nodes.

$$C_B(v) = \sum_{s \ne v \ne t} \frac{\sigma_{st}(v)}{\sigma_{st}}$$

Meaning. A high value is consistent with a bridge or brokerage position under the shortest-path model.

centrality_betweenness(student_interactions)

Stress Centrality

Stress centrality counts shortest paths passing through a node without fractional normalization across tied paths.

$$C_S(v) = \sum_{s \ne v \ne t} \sigma_{st}(v)$$

Meaning. A high value indicates that many shortest paths include the node.

centrality_stress(student_interactions)

Load Centrality

Load centrality distributes shortest-path load across alternative shortest routes: at each branch point a unit packet is split evenly among next hops on shortest paths.

$$C_L(v) = \sum_{s \ne v \ne t} \mathrm{load}_{st}(v)$$

Meaning. A high value indicates that a node carries a large share of geodesic routing load.

centrality_load(student_interactions)

Length-scaled Betweenness

Length-scaled betweenness counts the same brokered pairs as betweenness but weights each pair by the reciprocal of its distance.

$$C_{LS}(v) = \sum_{s \ne v \ne t} \frac{1}{d(s,t)} \cdot \frac{\sigma_{st}(v)}{\sigma_{st}}$$

Meaning. A high value indicates brokerage between pairs that were already close, which ordinary betweenness treats the same as brokerage across the graph.

centrality_length_scaled_betweenness(student_interactions)

Distance-decayed Betweenness

Distance-decayed betweenness discounts each brokered pair by a power of its distance, so the exponent tunes how local the measure is.

$$C_\delta(v) = \sum_{s \ne v \ne t} (d(s,t) - 1)^{-\delta} \cdot \frac{\sigma_{st}(v)}{\sigma_{st}}$$

Meaning. A high value indicates brokerage concentrated among nearby pairs. At $\delta = 0$ the measure is ordinary betweenness. Adjacent pairs have no intermediary, so the singularity at $d = 1$ is avoided.

centrality_delta_betweenness(student_interactions)

Ego Betweenness

Ego betweenness is betweenness computed inside a node’s own ego network.

$$C_{EB}(v) = \sum_{i < j \in N(v),\; A_{ij} = 0} \frac{1}{(A^2)_{ij}}$$

Meaning. A high value indicates a node that brokers among its own neighbours, which is what an egocentric survey can measure. A node with fewer than two neighbours scores 0.

centrality_ego_betweenness(student_interactions)

Bottleneck Centrality

Bottleneck centrality counts cases where a node is critical in shortest-path tree structures.

$$\mathrm{BN}(v) = \sum_{s \in V} p_s(v), \quad p_s(v) = 1 \text{ if } v \text{ carries} > \tfrac{n}{4} \text{ of the shortest-path tree } T_s$$

Meaning. A high value indicates frequent bottleneck position in local shortest-path trees.

centrality_bottleneck(student_interactions)

Bridging Centrality

Bridging centrality combines betweenness with a bridging coefficient.

$$\mathrm{Br}(v) = C_B(v) \cdot \beta(v), \quad \beta(v) = \frac{1/k_v}{\sum_{u \in N(v)} 1/k_u}$$

Meaning. A high value indicates a possible bridge position between locally distinct areas.

centrality_bridging(student_interactions)

Local bridging (legacy degree product)

Inverse focal degree multiplied by the bridging coefficient.

$$\mathrm{LBr}(v) = \frac{1}{k_v} \cdot \beta(v), \quad \beta(v) = \frac{1/k_v}{\sum_{u \in N(v)} 1/k_u}$$

Meaning. Retains the original cograph degree-only score. The formula uses degrees only.

centrality_local_bridging(student_interactions)

Percolation Centrality

Percolation centrality weights shortest-path brokerage by node states in a percolation process.

$$\mathrm{PC}(v) = \frac{1}{n - 2} \sum_{s \ne v \ne t} \frac{\sigma_{st}(v)}{\sigma_{st}} \cdot \frac{x_s}{\sum_{i \ne v} x_i}$$

Meaning. A high value indicates state-dependent path importance under the supplied state vector $x$.

centrality_percolation(student_interactions)

Flow Betweenness Centrality

Flow betweenness measures brokerage through maximum flows between pairs of nodes.

$$C_{FB}(v) = \sum_{s \ne v \ne t} f_{st}(v), \quad f_{st}(v) = \text{flow through } v \text{ in a max } s\text{-}t \text{ flow}$$

Meaning. A high value indicates importance for potential flow capacity between other nodes under the graph model.

centrality_flow_betweenness(student_interactions)

Current-Flow Betweenness Centrality

Current-flow betweenness measures how much electrical current between node pairs passes through a node.

$$C_{CFB}(v) = \frac{1}{(n - 1)(n - 2)} \sum_{s \ne t} \tau_{st}(v), \quad \tau_{st}(v) = \text{current through } v$$

Meaning. A high value indicates an intermediary position under an all-path current-flow model.

centrality_current_flow_betweenness(student_interactions)

Current-Flow Closeness Centrality

Current-flow closeness measures closeness with effective-resistance distances, treating the network as an electrical circuit.

$$C_{CFC}(v) = \frac{n - 1}{\sum_{u \ne v} R_{vu}}, \quad R_{vu} = \text{effective resistance between } v \text{ and } u$$

Meaning. A high value indicates closeness under an all-path flow model. It generally requires connected graphs.

centrality_current_flow_closeness(student_interactions)

Entropy Variation, Betweenness

Entropy variation of the betweenness distribution: the drop in its Shannon entropy when the node is removed.

$$EnV_b(i) = I_b(G) - I_b(G - i)$$

Meaning. A high value indicates a node whose removal concentrates shortest-path traffic on fewer nodes. Betweenness is recomputed per removal.

centrality(student_interactions, measures = "entropy_variation_betweenness")

Spectral, walk and influence

Trust-PageRank

Replace PageRank’s even split of a node’s score among its neighbours by a trust-value that mixes how similar the two nodes are with how large the receiver’s degree is.

$$TPR_i=\frac{1-\alpha}{n}+\alpha\sum_{j\in N_i}T(i,j)TPR_j,\qquad T(i,j)=(1-k)\frac{s(i,j)}{\sum_{l\in N_j}s(j,l)}+k\frac{d_i}{\sum_{l\in N_j}d_l}$$

Meaning. A high value indicates a node that receives a large share of a PageRank-style flow in which each node passes more of its score to neighbours that are similar to it and well connected. tpr_k sets the balance between similarity and degree, and tpr_alpha is the damping factor. A component without triangles returns NA with a warning, because the similarity is undefined there.

centrality_trust_pagerank(student_interactions)

Iterative resource allocation (IRA)

Give every node one unit of resource, hand it repeatedly to neighbours in proportion to the receiver’s centrality, and read the steady state.

$$I(t+1)=AI(t),\quad a_{ij}=\frac{\theta_i^{\alpha}}{\sum_{u\in\Gamma(j)}\theta_u^{\alpha}}\delta_{ij},\quad I(0)=\mathbf{1}$$

Meaning. A high value indicates a node that accumulates resource when every node repeatedly passes its resource to its neighbours in proportion to their centrality (ira_mass, default coreness). The scores of a connected component sum to its number of nodes. On a bipartite component with unequal sides the iteration oscillates, and a warning is raised.

centrality_ira(student_interactions)

Improved iterative resource allocation (IIRA)

The same resource iteration, with each node’s share scaled by how much of a spreading process it could actually carry.

$$a_{ij}=\bigl[1-(1-\beta)^{k_i}\bigr]\theta_i\Bigl(\sum_{u\in\Gamma(j)}\theta_u\Bigr)^{-1}\delta_{ij},\quad I(t)=A^{t}\mathbf{1}$$

Meaning. A high value indicates a node that accumulates resource when resource is passed to neighbours in proportion to their centrality and to their chance of being reached by a spreading process at rate iira_beta. The resource decays over the iira_steps iterations, so only the ranking is meaningful, and only within a connected component.

centrality_iira(student_interactions)

Multi-characteristics gravity model

Combine degree, coreness and eigenvector features in distance-decaying node interactions.

$$m_i=K_i+\alpha S_i+X_i,\quad \alpha=\max\{\mathrm{med}(K),\mathrm{med}(X)\}/\mathrm{med}(S),\quad MCGM_i=\sum_{j:0<d(i,j)\le R}m_i m_j/d(i,j)^2$$

Meaning. A high value indicates a node of large mass lying close to other nodes of large mass, where mass combines degree, coreness and eigenvector centrality, each scaled to its maximum. mcgm_alpha sets the weight of coreness, and only nodes within mcgm_radius steps (default 2) contribute.

centrality_mcgm(student_interactions)

SpectralRank

Outgoing influence after adding a ground node and optional node priors.

$$B=\begin{pmatrix}A+\operatorname{diag}(p)&\mathbf{1}\\\mathbf{1}^{T}&0\end{pmatrix},\quad Bs=\rho(B)s,\quad SR_i=s_i/\max_{j\le n+1}s_j$$

Meaning. A high value indicates a node with strong outgoing influence, measured by the leading eigenvector of the network augmented with a ground node linked to every node. sr_prior adds optional prior information for each node; with the default of zero the score is plain SpectralRank. Transpose the input to measure incoming influence.

centrality_spectralrank(student_interactions)

ControlRank

Smallest grounded eigenvalue after pinning each node in turn.

$$CR_i=\lambda_{\min}(((L+L^T)/2)_{-i,-i}),\quad L=\operatorname{diag}(A\mathbf{1})-A$$

Meaning. A high value indicates a node whose grounding leaves the rest of the network most tightly coupled, as measured by the smallest eigenvalue of the Laplacian with that node removed. Disconnected undirected graphs score zero at every node.

centrality_controlrank(student_interactions)

Node and Neighbor Layer Information

Propagate degree volume through a chosen number of neighbor steps.

$$b_i=\sum_{j:d(i,j)\le r}d_j,\quad r=\lceil L\rceil,\quad NINL_p=A^p b$$

Meaning. A high value indicates a node surrounded by high-degree nodes. Degrees are first summed within a radius and then propagated through ninl_order steps (default 3). With order 0 the score is the total degree of the node’s neighbourhood.

centrality_ninl(student_interactions)

Expected Force

Entropy of onward transmission opportunities after two infection events.

$$ExF(i)=-\sum_{j=1}^{J}p_j\log p_j,\quad p_j=D_j/\sum_kD_k$$

Meaning. A high value indicates a node from which an infection has many, and varied, ways to spread after its first two transmissions. The score is the entropy of these spreading opportunities.

centrality_expected_force(student_interactions)

Modified Expected Force

Expected Force adjusted for the seed degree.

$$ExF^M(i)=\log(\alpha d_i)ExF(i),\quad\alpha>1$$

Meaning. Expected force weighted by the logarithm of the node’s degree, so that among nodes with similar spreading opportunities the better-connected one scores higher. exf_alpha (default 2, and greater than 1) scales the degree inside the logarithm.

centrality_modified_expected_force(student_interactions)

Bridging capital

Loss of valued information walks after deleting each outgoing matrix entry.

$$Brid_i=\sum_j\sum_{s,t}v_{st}\sum_{h=1}^{T}[P^h-(P-P_{ij}E_{ij})^h]_{st}$$

Meaning. A high value indicates a node whose outgoing ties carry many of the network’s valued information walks, measured by the loss when each tie is removed in turn. Walks of up to bridging_steps steps (default 2) are counted, and bridging_values weights source-destination pairs. Edge weights are read as transmission probabilities.

centrality_bridging_capital(student_interactions, weighted = FALSE)

LineRank

Stationary edge-state probabilities aggregated at original endpoints.

$$p=cQ^T p+(1-c)\mathbf{1}/m,\quad LR_v=\sum_{e\text{ incident to }v}p_e$$

Meaning. A high value indicates a node whose ties are visited often by a random walk that moves from tie to tie. Tie probabilities are summed at their end nodes; linerank_aggregation = "weight" also multiplies them by the tie weights. damping is the usual random-walk damping factor.

centrality_linerank(student_interactions)

Random walk decay

Discounted first arrival at each node, summed over starting-node weights.

$$RWD_v=\sum_u b_u\,\mathbb{E}_u[a^{T_v};T_v<\infty],\quad 0\leq a<1$$

Meaning. A high value indicates a node that random walks starting elsewhere reach early. Each first arrival is discounted by rwd_decay (default 0.5) per step, and starting nodes can be weighted with rwd_node_weights. A node’s own outgoing ties cannot change its own score.

centrality_random_walk_decay(student_interactions)

Graph regularization centrality

Reciprocal retention of a unit impulse under weighted Laplacian smoothing.

$$GRC_i=1/[(I+\gamma L)^{-1}]_{ii},\quad L=D-W,\quad \gamma\geq0$$

Meaning. A high value indicates a node whose signal spreads quickly across the network under Laplacian smoothing, so that little of it is retained at the node. grc_gamma (default 1) sets the strength of smoothing. Scores range from 1 to the size of the node’s component, and isolates score 1.

centrality_graph_regularization(student_interactions)

Adaptive LeaderRank

Stationary resource scores with every destination weighted by its original H-index and a ground node with H-index one.

$$h_g=1,\quad w_{ji}=a_{ji}h_i,\quad s_i=\sum_j\frac{w_{ji}}{\sum_k w_{jk}}s_j,\quad \sum_{i\cup g}s_i=N$$

Meaning. A high value indicates a node that attracts a large share of a random resource flow in which each node sends more to neighbours with a high H-index. alr_h_mode sets how H-indices are computed on directed graphs.

centrality_adaptive_leaderrank(student_interactions)

Weighted LeaderRank

Stationary resource scores with a ground node that distributes according to original in-degree powers.

$$w_{gi}=(k_i^{in})^{\alpha},\quad w_{ig}=1,\quad s_i=\sum_j\frac{w_{ji}}{\sum_l w_{jl}}s_j,\quad \sum_{i\cup g}s_i=N+1$$

Meaning. A high value indicates a node that attracts a large share of a random resource flow in which a ground node, linked to every node, sends more to nodes with high in-degree. wlr_alpha sets how strongly in-degree is favoured; at zero the measure is ordinary LeaderRank.

centrality_weighted_leaderrank(student_interactions)

Node Resistance Curvature

A geometric descriptor based on electrical resistance, evaluated separately within each connected component.

$$p_i=1-\frac{1}{2}\sum_{j\sim i}w_{ij}R_{ij}$$

Meaning. Low or negative values identify tree-like junctions that hold parts of the network together; higher values indicate nodes with redundant connections. Edge weights are read as conductances. The scores within each connected component sum to one.

centrality_resistance_curvature(student_interactions)

Dynamics-Sensitive Centrality

A finite-time linearized spreading score with explicit spreading and recovery rates, on the simple undirected skeleton.

$$S(T)=\sum_{r=0}^{T-1}\beta A[\beta A+(1-\mu)I]^r\mathbf{1}$$

Meaning. Higher scores indicate more cumulative spreading activity in this approximation. Values can exceed the number of nodes. Defaults are beta=0.1, mu=1, T=5. Recovery mu=1 recovers finite diffusion; mu=0 selects the SI case. Isolates and horizon zero score zero; overflow raises an error.

centrality_dynamics_sensitive(student_interactions)

Finite-Horizon Diffusion Centrality

Total weighted walks of lengths 1 through T starting at a node, allowing repeated visits and returns.

$$DC(A;q,T) = \sum_{t=1}^{T}(qA)^t\mathbf{1}$$

Meaning. High values indicate more weighted walk activity from the source. Probability interpretation requires entries of qA in [0,1]. Defaults q=1 and T=3 are cograph choices. Directed arcs carry information outwards. Overflow raises an error.

centrality_diffusion_centrality(student_interactions)

Dynamical Importance

Relative loss of adjacency spectral radius after removing the node, recomputed directly for each deletion.

$$I_i = \frac{\rho(A)-\rho(A_{-i})}{\rho(A)}$$

Meaning. A high value indicates a node whose removal most reduces the network’s spectral radius, which governs how easily spreading processes take off. Each deletion is recomputed exactly.

centrality_dynamical_importance(student_interactions)

Eigenvector Centrality

Eigenvector centrality gives high scores to nodes connected to other high-scored nodes.

$$A\,\mathbf{x} = \lambda_{\max}\,\mathbf{x}, \quad C_E(v) = x_v$$

Meaning. A high value indicates embeddedness in a central neighbourhood. Reading it as influence requires a relation that transmits influence.

centrality_eigenvector(student_interactions)

PageRank Centrality

PageRank is a damped random-walk centrality. Nodes score highly when a random walker reaches them often.

$$\mathrm{PR}(v) = \frac{1 - \alpha}{n} + \alpha \sum_{u \,:\, u \to v} \frac{\mathrm{PR}(u)}{k_u^{\mathrm{out}}}, \quad \alpha = 0.85$$

Meaning. A high value indicates random-walk prominence under the chosen damping, direction, and weight conventions.

centrality_pagerank(student_interactions)

Authority Centrality

Authority centrality is part of HITS. Authorities are nodes pointed to by good hubs.

$$\mathbf{a} = A^{\top} \mathbf{h}, \quad \mathbf{h} = A\,\mathbf{a} \;\Rightarrow\; \mathbf{a} = \text{principal eigenvector of } A^{\top} A$$

Meaning. A high value indicates incoming support from nodes that point to good authorities.

centrality_authority(student_interactions)

Hub Centrality

Hub centrality is the HITS counterpart to authority. Hubs point to good authorities.

$$\mathbf{h} = \text{principal eigenvector of } A\,A^{\top}$$

Meaning. A high value indicates outgoing ties to high-authority nodes.

centrality_hub(student_interactions)

SALSA Centrality

SALSA is a stochastic link-analysis method related to HITS for directed graphs.

$$\mathbf{a} = \text{principal eigenvector of } A_c^{\top} A_r, \quad A_r, A_c = \text{row- and column-normalised } A$$

Meaning. A high value indicates directed authority under the SALSA random-walk model.

centrality_salsa(student_interactions)

LeaderRank Centrality

LeaderRank is a PageRank-like directed ranking method that adds a ground node $g$ linked both ways to every node; the stationary random-walk mass on $g$ is then redistributed equally.

$$\mathbf{s}^{*} = \text{stationary distribution of the walk on } G \cup \{g\}, \quad C(v) = s^{*}_v + \tfrac{1}{n} s^{*}_g$$

Meaning. A high value indicates directed prestige under the LeaderRank model.

centrality_leaderrank(student_interactions)

Alpha Centrality

Alpha centrality is an eigenvector-like measure that includes exogenous input.

$$\mathbf{x} = (I - \alpha A^{\top})^{-1} \mathbf{e}$$

Meaning. A high value indicates recursive prominence under the chosen attenuation and exogenous assumptions.

centrality_alpha(student_interactions)

Bonacich Power Centrality

Bonacich power centrality scores nodes using the centrality of their neighbours and a parameter $\\beta$ controlling dependence.

$$\mathbf{c}(\alpha, \beta) = \alpha (I - \beta A)^{-1} A\,\mathbf{1}$$

Meaning. A high value indicates a favourable recursive position under the selected Bonacich parameterization.

centrality_power(student_interactions)

Katz Centrality

Katz centrality counts walks from all nodes to a focal node, attenuating longer walks.

$$\mathbf{x} = (I - \alpha A^{\top})^{-1} \mathbf{1}, \quad \alpha = 0.1$$

Meaning. A high value indicates that many short and longer walks reach the node. The attenuation parameter must be valid for the graph.

centrality_katz(student_interactions)

Hubbell Centrality

Hubbell centrality is an input-output centrality where status is recursively reinforced through ties.

$$\mathbf{x} = (I - w W)^{-1} \mathbf{1}, \quad w = 0.5,\; W = \text{weighted adjacency}$$

Meaning. A high value indicates recursive prominence under the chosen weight factor. Some parameter settings make the system singular.

centrality_hubbell(student_interactions)

Subgraph Centrality

Subgraph centrality measures participation in closed walks, with shorter closed walks weighted more strongly.

$$\mathrm{SC}(v) = \sum_{k=0}^{\infty} \frac{(A^k)_{vv}}{k!} = (e^{A})_{vv}$$

Meaning. A high value indicates embeddedness in many closed walk structures.

centrality_subgraph(student_interactions)

Laplacian Centrality

Laplacian centrality measures a node’s contribution to the graph’s Laplacian energy.

$$C_L(v) = \frac{E_L(G) - E_L(G \setminus v)}{E_L(G)}, \quad E_L(G) = \sum_i \mu_i^2 \;\; (\mu_i = \text{Laplacian eigenvalues})$$

Meaning. A high value indicates a large local structural contribution under the Laplacian energy definition.

centrality_laplacian(student_interactions)

Communicability Centrality

Communicability centrality uses the matrix exponential to summarise walk-based communication potential.

$$C_{\mathrm{Comm}}(v) = \sum_{u \in V} (e^{A})_{vu}$$

Meaning. A high value indicates many walk-based routes to other nodes, with shorter walks weighted more strongly.

centrality_communicability(student_interactions)

Communicability Betweenness Centrality

Communicability betweenness measures walk-based communicability between other pairs through a node.

$$C_{CB}(v) = \frac{1}{(n-1)(n-2)} \sum_{s \ne t \ne v} \frac{G_{st} - G_{st}^{(v)}}{G_{st}}, \quad G = e^{A}$$

Meaning. A high value indicates a node that lies on many walks between other nodes. $G^{(v)}$ is computed with $v$ removed.

centrality_communicability_betweenness(student_interactions)

Random Walk Centrality

Random walk centrality uses expected random-walk access times as distances.

$$C_{RW}(v) = \frac{1}{\sum_{u \ne v} \tilde{m}_{vu}}, \quad \tilde{m}_{vu} = \tfrac{1}{2}\bigl( m_{vu} + m_{uv} \bigr)$$

Meaning. A high value indicates that the node is reached efficiently under a random-walk process. $m_{uv}$ is the mean first-passage time.

centrality_random_walk(student_interactions)

Markov Centrality

Markov centrality is based on mean first-passage times in a Markov process on the graph.

$$C_{\mathrm{Mk}}(v) = \frac{1}{\frac{1}{n} \sum_{u} m_{uv}}, \quad m_{uv} = \text{mean first-passage time } u \to v$$

Meaning. A high value indicates that the node is reached quickly on average under the Markov process.

centrality_markov(student_interactions)

Immediate Effects Centrality (IEC)

Score a node by how quickly everyone else’s influence reaches it, along a chain in which every actor also listens to itself.

$$c_{IEC}(j)=\Bigl(\frac{\sum_{i \ne j} m_{ij}}{n-1}\Bigr)^{-1},\qquad \mathbf{M}=(\mathbf{I}-\mathbf{Z}+\mathbf{E}\mathbf{Z}_{dg})\operatorname{diag}(1/c),\qquad \mathbf{Z}=(\mathbf{I}-\mathbf{W}+\mathbf{1}c')^{-1}$$

Meaning. A high value indicates a node that the influence of all other nodes reaches quickly, through short chains of influence. The score is defined only when every node can reach every other; otherwise every node returns NA with a warning.

centrality_iec(student_interactions)

Second-Order Centrality

Second-order centrality summarises variability in random-walk return times.

$$\mathrm{SO}(v) = \operatorname{sd}_{u}\bigl( m_{uv} \bigr)$$

Meaning. The statistic describes the variability of a random walk’s return times to the node; lower values are more central.

centrality(student_interactions, measures = "second_order")

Information Centrality

Information centrality measures centrality through resistance distance, the information carried by all paths between two nodes.

$$C_I(v) = \left( C_{vv} + \frac{T - 2 R_v}{n} \right)^{-1}, \quad C = (D - A + J)^{-1},\; T = \operatorname{tr} C,\; R_v = \textstyle\sum_j C_{vj}$$

Meaning. A high value indicates a central position under the information-flow model.

centrality_information(student_interactions)

Nonbacktracking Centrality

Nonbacktracking centrality scores nodes using walks that do not immediately return along the edge just traversed (the Hashimoto matrix $B$).

$$B_{(i \to j),\,(k \to l)} = \delta_{jk}\,(1 - \delta_{il}), \quad C(v) \propto \text{aggregated leading eigenvector of } B$$

Meaning. A high value indicates walk-based prominence after reducing immediate backtracking inflation.

centrality(student_interactions, measures = "nonbacktracking")

Diffusion Degree

The default method adds the scaled degree of the focal node and its neighbours. On a simple undirected graph:

$$DD(i) = \lambda\left(k_i + \sum_{j\in N(i)} k_j\right)$$

Meaning. High values indicate local connectivity through the node and its neighbours. With diffusion_method = "power_series" (automatic for TNA inputs), the function instead returns row sums of W + W^2 + … + W^n. That variant fixes the horizon at n and ignores lambda and mode.

centrality_diffusion(student_interactions)

Infection Centrality

Infection centrality estimates spreading potential through infection-style self-avoiding walks with attenuation.

$$\mathrm{Inf}(v) = \sum_{d=1}^{L} \beta^{\,d+1} (1 - \mu)^{d}\, w_d(v), \quad \beta = 0.8,\; \mu = 0,\; L = 6$$

Meaning. A high value indicates a favourable position under the assumed infection process. $w_d(v)$ counts length-$d$ self-avoiding walks from $v$.

centrality(student_interactions, measures = "infection")

Edge Percolated Component

The edge percolated component averages the size of the component a node ends up in when edges survive at random.

$$\mathrm{EPC}(v) = \frac{1}{R\,n} \sum_{r=1}^{R} |C_r(v)|, \quad \Pr(\text{edge survives}) = 1 - t$$

Meaning. A high value indicates a node that stays connected to a large share of the network under random link failure. The value is a Monte Carlo estimate; set epc_seed to make it reproducible.

centrality_epc(student_interactions)

VoteRank Centrality

VoteRank is an iterative voting algorithm for ranking spreader candidates: each round the highest-voted node is selected, its voting ability is zeroed, and its neighbours’ voting abilities are reduced.

Meaning. A high value indicates early selection by the VoteRank rule.

centrality_voterank(student_interactions)

Expected Influence 1-Step

Expected influence 1-step sums signed edge weights adjacent to a node.

$$\mathrm{EI}_1(v) = \sum_{u \in V} W_{vu}, \quad W = \text{signed weight matrix}$$

Meaning. A high positive value indicates strong positive immediate signed connectivity. This is mainly meaningful for signed networks.

centrality_expected_influence_1(student_interactions)

Expected Influence 2-Step

Expected influence 2-step extends signed influence to one- and two-step paths.

$$\mathrm{EI}_2(v) = \mathrm{EI}_1(v) + \sum_{u \in V} W_{vu}\,\mathrm{EI}_1(u)$$

Meaning. A high positive value indicates positive signed connectivity through direct and indirect paths. Interpretation requires a signed network.

centrality_expected_influence_2(student_interactions)

Spanning Tree Centrality

Spanning tree centrality summarises a node’s contribution across spanning-tree structures via the Laplacian pseudoinverse $L^{+}$.

$$\mathrm{ST}(v) = \frac{1}{L^{+}_{vv}}, \quad L^{+} = \text{Moore-Penrose pseudoinverse of } L$$

Meaning. A high value indicates structural participation across many tree-like ways of connecting the graph.

centrality(student_interactions, measures = "spanning_tree")

Shapley Value, Game 1

Shapley value of the node in the coalition game whose worth is the number of nodes a coalition covers within one hop.

$$SV_1(v) = \sum_{u \in \{v\} \cup N(v)} \frac{1}{1 + k_u}$$

Meaning. A high value indicates a node whose presence adds much one-hop coverage to a typical coalition. Values sum to $n$.

centrality_shapley_game1(student_interactions)

Shapley Value, Game 2

Shapley value in the game where a node is covered once at least $k$ coalition members are adjacent to it.

$$SV_2(v) = \min\!\left(1, \frac{k}{1 + k_v}\right) + \sum_{u \in N(v)} \max\!\left(0, \frac{k_u - k + 1}{k_u (1 + k_u)}\right)$$

Meaning. A high value indicates a node that helps push many neighbours over the $k$-neighbour threshold. With $k = 1$ this is game 1.

centrality_shapley_game2(student_interactions)

Shapley Value, Game 3

Shapley value in the game where a coalition covers every node within a hop cutoff.

$$SV_3(v) = \sum_{u \in \{v\} \cup N_d(v)} \frac{1}{1 + |N_d(u)|}, \quad N_d(u) = \{w : d(u, w) \le d_{cut}\}$$

Meaning. A high value indicates a node that reaches many otherwise hard-to-reach nodes within the cutoff. With cutoff 1 this is game 1.

centrality_shapley_game3(student_interactions)

Rumor Centrality

Rumor centrality counts the spreading orders that could have started at a node; on a general graph it is evaluated on the node’s breadth-first tree.

$$\log R(v) = \log N! - \sum_{u} \log T^v_u$$

Meaning. A high value indicates a plausible origin of a spread, typically a node near the centre. Returned on the log scale.

centrality_rumor(student_interactions)

DegreeDiscountIC

DegreeDiscountIC is the greedy seed-selection order under degree discounting for the independent-cascade model.

$$dd_v = d_v - 2 t_v - (d_v - t_v)\, t_v\, p$$

Meaning. A high score indicates an early selection: the first node selected scores 1, the last $1/n$. Ties follow node order.

centrality_degree_discount(student_interactions)

SingleDiscount

SingleDiscount is the greedy seed-selection order where each neighbour of a new seed discounts its degree by one.

$$dd_v = d_v - t_v$$

Meaning. A high score indicates an early selection. Equivalent to repeatedly removing the highest-degree node.

centrality_single_discount(student_interactions)

NCVoteRank

NCVoteRank is VoteRank with each voter’s ability weighted by its normalised neighbourhood coreness, and two-hop weakening after each election.

$$s_u = \sum_{v \in N(u)} va_v \,[\theta + (1 - \theta)\, nc_v]$$

Meaning. A high score indicates an early election. With $\theta = 1$ and two-hop weakening switched off, this is VoteRank.

centrality_ncvoterank(student_interactions)

WVoteRank

WVoteRank is VoteRank for weighted graphs: votes are weighted by edge weight and the score takes a square root.

$$s_v = \sqrt{k_v \sum_{u \in N(v)} va_u w_{vu}}$$

Meaning. A high score indicates an early election. Neighbours of an elected node lose $1/\langle w \rangle$, with $\langle w \rangle$ the average strength.

centrality_wvoterank(student_interactions)

EnRenew

EnRenew elects the node whose neighbours supply the most entropy, then renews the entropies around it.

$$E_v = \sum_{u \in N(v)} -p_{uv} \ln p_{uv}, \quad p_{uv} = \frac{k_u}{\sum_{l \in N(v)} k_l}$$

Meaning. A high score indicates an early election. Terms within the renewal radius are scaled by $1 - 1/(2^{d-1} \ln\langle k \rangle)$.

centrality_enrenew(student_interactions)

VoteRank++

VoteRank++ starts abilities from degree, splits votes in proportion to neighbour degree, and suppresses abilities multiplicatively after each election.

$$s_v = \sqrt{k_v \sum_{u \in N(v)} va_u\, w_{u \to v}}, \quad va_v^{(0)} = \ln(1 + k_v / k_{\max})$$

Meaning. A high score indicates an early election. Abilities are multiplied by $\lambda$ one step away and $\sqrt{\lambda}$ two steps away.

centrality_voterank_plus(student_interactions)

Node Contraction

Node contraction importance measures how much the network’s cohesion rises when a node and its neighbours are merged into one.

$$IMC(v) = 1 - \frac{\partial(G)}{\partial(G_v)}, \quad \partial(G) = \frac{1}{N \bar{L}}$$

Meaning. A high value indicates a node whose contraction shortens paths most.

centrality_node_contraction(student_interactions)

Improved Node Contraction

Improved node contraction adds the contraction scores of a node’s edges, computed on the line graph.

$$IIMC(v) = \alpha\, IMC(v) + \beta \sum_{e \ni v} IMC_{L(G)}(e), \quad \alpha / \beta = 5$$

Meaning. A high value indicates a node that is important both itself and through its edges. Values can exceed 1.

centrality_node_contraction_improved(student_interactions)

Two-Way Random Walk Betweenness

Two-way random walk betweenness counts, over all node pairs, how often a node lies on the most likely two-step out-and-back route.

$$T_{ij}[t, k] = P_{itj} P_{jki}, \quad P_{itj} = \frac{w_{it} w_{tj}}{d_i d_j}$$

Meaning. A high count indicates a node on many dominant two-way routes. Cost grows as $n^4$.

centrality_two_way_rw(student_interactions)

Neighbourhood structure and cohesion

Degree and importance of lines (DIL)

Add to a node’s degree the share it can claim of the importance of the lines that touch it, where a line matters when its endpoints reach far beyond it and few triangles offer a way round.

$$L_{v_i}=k_i+\sum_{v_j\in\Gamma_i}W_{v_iv_j},\qquad W_{v_iv_j}=I_{e_{ij}}\frac{k_i-1}{k_i+k_j-2},\qquad I_{e_{mn}}=\frac{(k_m-p-1)(k_n-p-1)}{p/2+1}$$

Meaning. A high value indicates a node with many ties, several of them important: ties whose endpoints reach far beyond them and that few triangles bypass. The score is at least the node’s degree, and equals it in complete graphs and stars.

centrality_dil(student_interactions)

Lhc index

Score a node by how much degree, inflated by each contributor’s share of the network’s triangles, sits within a couple of steps of its neighbours.

$$Lhc(v)=\sum_{w\in\tau(v)}C(w),\qquad C(v)=\sum_{u\in\Phi(v)}\frac{k_u\bigl(1+TP(u)\bigr)}{d^{2}(uv)},\qquad TP(u)=\frac{NTS(u)}{TNTS}$$

Meaning. A high value indicates a node whose surroundings, within lhc_radius steps (default 2), hold many well-connected nodes that sit on many triangles. A node of modest degree can therefore outscore a busier one if its neighbours are well embedded. On a graph without triangles the index reduces to a distance-discounted sum of degrees.

centrality_lhc(student_interactions)

Hybrid characteristic centrality (HCC)

Add a node’s blended local degree to how late a repeated minimum-degree peel gets round to removing it.

$$HCC(u)=\frac{k^{ex}(u)}{k^{ex}_{\max}}+\frac{pos(u)}{pos_{\max}},\qquad k^{ex}(u)=\delta k(u)+(1-\delta)\!\!\sum_{v\in\phi(u)}\!\!k(v)$$

Meaning. A high value indicates a node that combines a high degree, blended with its neighbours’ degrees, with a position deep in the network, reached late when low-degree nodes are repeatedly peeled away. hcc_delta (default 0.5) weights the node’s own degree against its neighbours’. Scores range from 0 to 2.

centrality_hcc(student_interactions)

Extended hybrid characteristic centrality (EHCC)

Collect a node’s own hybrid characteristic score together with every neighbour’s.

$$EHCC(u)=HCC(u)+\sum_{v\in\phi(u)}HCC(v)$$

Meaning. A high value indicates a node that, together with its neighbours, scores highly on hybrid characteristic centrality. It rewards nodes with well-connected, deeply embedded neighbours, whatever the node’s own position. An isolate scores its own hybrid characteristic centrality.

centrality_ehcc(student_interactions)

KED method

Weight the degree by how evenly a node’s neighbours carry its local paths, then by how many second-step paths there are.

$$KED(i)=k_i\bigl(1+H_i\bigr)\exp\!\Bigl(\frac{K_i}{N}\Bigr),\qquad H_i=\frac{\sum_{j\in N(i)}-p_j\log p_j}{\log k_i},\quad p_j=\frac{k_j}{K_i},\quad K_i=\sum_{j\in N(i)}k_j$$

Meaning. A high value indicates a node with many neighbours whose onward connections are numerous and evenly spread among them. Scores depend on the number of nodes in the whole graph, so only rankings are comparable across graphs of different size.

centrality_ked(student_interactions)

Local neighbor contribution (LNC)

Multiply what a node contributes on its own by what its neighbourhood contributes to it.

$$LNC(i)=\underbrace{d_i\bigl(1-1/d_i\bigr)^{d_i-1}}_{ownCon(i)}\cdot\underbrace{d_i^{2}\frac{\sum_{j\in N(i)}d_j}{n-1}}_{neiCon(i)},\qquad 0^0:=1$$

Meaning. A high value indicates a node with many neighbours that are themselves well connected. A node with a single neighbour scores its neighbour’s degree centrality. Scores depend on the number of nodes in the whole graph, so only rankings are comparable across graphs of different size.

centrality_lnc(student_interactions)

Neighborhood (neighbor distance) centrality

Add up a benchmark centrality over the non-backtracking walks that leave the node, discounted once per step.

$$C^n_i(\theta)=\theta_i+\sum_{k=1}^{n}a^k\!\!\sum_{w\in W_k(i)}\!\!\theta_{\mathrm{end}(w)},\quad W_k(i)=\{\text{non-backtracking walks of length }k\text{ from }i\}$$

Meaning. A high value indicates a node that reaches many central nodes, by nd_mass (default degree), within a few steps. Each step of distance is discounted by nd_decay (default 0.2), up to nd_order steps (default 2). With either set to zero, the score is the base centrality itself.

centrality_neighbor_distance(student_interactions)

Coleman-Theil hierarchy

Concentration of Burt’s dyadic constraints over a node’s contacts.

$$H_i=\frac{\sum_{j\in N(i)}r_{ij}\log(r_{ij})}{d_i\log d_i},\quad r_{ij}=c_{ij}/\operatorname{mean}_{k\in N(i)}c_{ik}$$

Meaning. A high value indicates that the constraint on a node is concentrated in a few of its contacts. Scores lie between 0 and 1; a node with one contact scores 1 and an isolate 0.

centrality_coleman_theil(student_interactions)

Maximal Clique Centrality

Sum of factorial contributions from maximal cliques containing the node, on the simple undirected skeleton.

$$MCC(v) = \sum_{C\in\mathcal{M}(v),\,|C|\geq2} (|C|-1)!$$

Meaning. Larger cliques contribute much more than edges or triangles. Contained cliques are excluded. Isolates score 0 by explicit convention. Exponential enumeration is held back from the default all tier; raw-score overflow raises an error.

centrality_mcc(student_interactions)

Node Truss Number

The largest truss number of an incident edge, on the simple undirected skeleton.

$$t(v) = \max\{k : v \in V(T_k)\},\quad T_k:\ \text{each edge has at least } k-2\text{ triangles}$$

Meaning. Higher values indicate triangle-supported cohesion. Tree vertices score 2, a k-clique scores k, and isolates score 0.

centrality_truss(student_interactions)

Mixed Degree Decomposition

Shell thresholds from residual degree plus attenuated exhausted degree.

$$k_i^{m} = k_i^{r} + \lambda k_i^{e}$$

Meaning. Higher means membership of a stronger mixed-degree shell. Lambda 0 gives coreness; lambda 1 gives degree. Default 0.7; scores can be fractional.

centrality_mdd(student_interactions)

Bridging Coefficient

The reciprocal-degree ratio before multiplication by betweenness.

$$B(i) = \frac{1/k_i}{\sum_{j\in N(i)}1/k_j}$$

Meaning. Higher values identify low-degree nodes adjacent to high-degree nodes. Isolates score 0. Uses the simple undirected skeleton.

centrality_bridging_coefficient(student_interactions)

Godfather Index

The number of unconnected unordered pairs of a node’s neighbours.

$$GF(i) = {k_i\choose 2} - t_i$$

Meaning. Higher values indicate more local brokerage opportunities. Here t_i counts focal triangles. Uses the simple undirected skeleton.

centrality_godfather(student_interactions)

Supported Relationships

The number of neighbours sharing at least one common neighbour with the node.

$$S(i) = |\{j\in N(i):(A^2)_{ij}>0\}|$$

Meaning. Higher values mean more triangle-supported relationships; a relationship counts once even if several common neighbours support it. Uses the simple undirected skeleton.

centrality_support(student_interactions)

Transitivity Centrality

Transitivity measures local clustering: whether a node’s neighbours are connected to each other.

$$c(v) = \frac{2\, t_v}{k_v (k_v - 1)}, \quad t_v = \text{number of triangles through } v$$

Meaning. A high value indicates locally closed neighbourhoods. This can represent cohesion or redundancy, depending on the relation.

centrality_transitivity(student_interactions)

Constraint Centrality

Constraint measures how redundant a node’s contacts are, following Burt’s structural holes framework.

$$C(v) = \sum_{u \in N(v)} \left( p_{vu} + \sum_{q \ne v, u} p_{vq}\, p_{qu} \right)^2, \quad p_{vu} = \text{proportional tie strength}$$

Meaning. A high value indicates a locally constrained ego network. Lower constraint may indicate structural holes, depending on theory and relation type.

centrality_constraint(student_interactions)

Effective Size Centrality

Effective size estimates the number of nonredundant contacts in a node’s ego network (Burt).

$$\mathrm{ES}(v) = k_v - \frac{1}{k_v} \sum_{u \in N(v)} |N(v) \cap N(u)|$$

Meaning. A high value indicates relatively nonoverlapping contacts.

centrality_effective_size(student_interactions)

Topological Coefficient Centrality

Topological coefficient centrality measures shared-neighbour overlap.

$$T(v) = \frac{\operatorname{avg}_{u}\, J(v, u)}{k_v}, \quad J(v, u) = \text{number of neighbours shared by } v \text{ and } u$$

Meaning. A high value indicates neighbourhood overlap or topological similarity.

centrality_topological_coefficient(student_interactions)

Diversity Centrality

Diversity centrality measures entropy in the distribution of edge weights around a node.

$$\mathrm{Div}(v) = \frac{-\sum_{u \in N(v)} p_{vu} \log_2 p_{vu}}{\log_2 k_v}, \quad p_{vu} = \frac{w_{vu}}{\sum_{u'} w_{vu'}}$$

Meaning. A high value indicates that weighted ties are relatively evenly distributed.

centrality_diversity(student_interactions)

Cross-Clique Centrality

Cross-clique centrality counts how many cliques contain a node.

$$X(v) = |\{\, Q \in \mathcal{Q}(G) : v \in Q \,\}|, \quad \mathcal{Q}(G) = \text{set of cliques}$$

Meaning. A high value indicates participation in many fully connected local groups.

centrality_cross_clique(student_interactions)

Coreness Centrality

Coreness assigns nodes to k-core shells.

$$C_{\mathrm{core}}(v) = \max \{\, k : v \in (k\text{-core of } G) \,\}$$

Meaning. A high value indicates membership in a dense core.

centrality_coreness(student_interactions)

Onion Centrality

Onion centrality assigns nodes to layers from onion decomposition, a fine-grained extension of k-core decomposition.

$$\text{layer}(v) = \text{iteration index at which } v \text{ is peeled by the onion decomposition}$$

Meaning. A high layer indicates that the node remains until later stages of the peeling process.

centrality(student_interactions, measures = "onion")

K-Reach Centrality

K-reach centrality counts nodes reachable within path length $k$.

$$C_{kR}(v) = |\{\, u : 0 < d(v, u) \le k \,\}|, \quad k = 3$$

Meaning. A high value indicates broad reach within a fixed local radius. Results depend on the chosen $k$.

centrality_kreach(student_interactions)

s-shell Index

The s-shell index peels the graph by node strength built from asymmetric topological link weights, generalising k-shell.

$$w_{ij} = 1 + (k_i\, k^{out}_j)^a, \quad s_i = \sum_{j \in N(i)} w_{ij}$$

Meaning. A high shell index indicates a node deep in the strength-based core. With $a = 0$ the shells are the dense ranks of k-core.

centrality_s_shell(student_interactions)

Weighted k-shell

The weighted k-shell peels the graph by a generalised degree that mixes degree and strength.

$$k'_v = \big(k_v^{\alpha} s_v^{\beta}\big)^{1 / (\alpha + \beta)}$$

Meaning. A high shell index indicates a node deep in the weighted core. Unit weights give the k-core number.

centrality_weighted_kshell(student_interactions)

Renewed Coreness

Renewed coreness is the k-core number after removing links that lead nowhere new.

$$D_{ij} = \frac{|N(j) \setminus N[i]| + |N(i) \setminus N[j]|}{2}, \quad \text{keep } D_{ij} \ge 2$$

Meaning. A high value indicates a core node whose links reach beyond shared neighbourhoods. An isolated clique scores 0.

centrality_renewed_coreness(student_interactions)

s-core Index

The s-core index is the weighted k-core: the largest strength threshold whose core still contains the node.

$$s\text{-core}(s) = \text{maximal } H \subseteq G \text{ with } s_i^H \ge s \;\; \forall i \in H$$

Meaning. A high value indicates a node deep in the strength-based core. Unit weights give the k-core number exactly.

centrality_s_core(student_interactions)

Local Efficiency

Local efficiency is how well a node’s neighbours still communicate once the node itself is gone, using only the links among them.

$$E_{loc}(v) = \frac{1}{k_v (k_v - 1)} \sum_{i \ne j \in N(v)} \frac{1}{d_{ij}^{\,G_v}}$$

Meaning. A high value indicates a fault-tolerant neighbourhood; a node whose neighbours are mutually unconnected scores 0. Note that igraph::local_efficiency() measures those distances through the rest of the network instead, so it reports larger values.

centrality_local_efficiency(student_interactions)

Directed prestige and hierarchy

BG-index (beta power)

Expected number of times a node is selected as a predecessor.

$$\beta^+(i)=\sum_{i\to j}1/d^-(j),\quad\beta^-(i)=\sum_{j\to i}1/d^+(j)$$

Meaning. A high value indicates a node that is the sole predecessor, or one of few, of many others: each node divides one unit equally among the nodes that point to it. The default credits senders; beta_direction = "negative" credits receivers. The two coincide on undirected graphs.

centrality_beta_measure(student_interactions)

Prestige Domain Centrality

Prestige domain centrality counts how many nodes can reach a focal node in a directed graph.

$$P(v) = |\{\, u \ne v : u \rightsquigarrow v \,\}|$$

Meaning. A high value indicates a large incoming reach domain.

centrality_prestige_domain(student_interactions)

Prestige Domain Proximity Centrality

Prestige domain proximity measures how close nodes in the prestige domain are to the focal node.

$$C(v) = \frac{|R^{-}(v)|^2}{(n - 1) \sum_{u \in R^{-}(v)} d(u, v)}, \quad R^{-}(v) = \{ u \ne v : u \rightsquigarrow v \}$$

Meaning. A high value indicates that many predecessors can reach the node through short directed paths.

centrality_prestige_domain_proximity(student_interactions)

Local Reaching Centrality

Local reaching centrality measures how much of the network is reachable from a node through directed paths.

$$\mathrm{LRC}(v) = \frac{|\{\, u : v \rightsquigarrow u \,\}|}{n - 1}$$

Meaning. A high value indicates broad directed reach from the node.

centrality_reaching_local(student_interactions)

Pairwise Disconnectivity Centrality

Pairwise disconnectivity measures how directed reachability between pairs changes when a node is removed.

$$\mathrm{Dis}(v) = \frac{P(G) - P(G \setminus v)}{P(G)}, \quad P(G) = |\{ (s, t) : s \rightsquigarrow t \}|$$

Meaning. A high value indicates structural importance for preserving directed reachability.

centrality_pairwisedis(student_interactions)

Trophic Level Centrality

Trophic level estimates hierarchical position in a directed flow network.

$$(I - W)\,\mathbf{s} = \mathbf{1}, \quad W_{ji} = \frac{a_{ij}}{k_j^{\mathrm{in}}}, \quad \text{basal nodes have level } 1$$

Meaning. A higher value indicates a higher position in the inferred directed hierarchy. This is appropriate only for flow-like relations.

centrality(student_interactions, measures = "trophic_level")

The measures below need a community membership vector. Build one with detect_communities() (walktrap works on the directed student_interactions graph), then pass it as membership =:

comm   <- detect_communities(student_interactions, method = "walktrap")
groups <- setNames(comm$community, comm$node)
centrality_participation(student_interactions, membership = groups)
#>        Ac        Ad        Fi        Ik        Vx        Rt        Km        Gj 
#> 0.6078972 0.5450000 0.4965278 0.5612245 0.6213018 0.5150000 0.4628099 0.5540166 
#>        Bd        Ce        Oq        Ya        Mo        Hj        Tv        Eg 
#> 0.4861111 0.4600000 0.4897959 0.4733728 0.5416667 0.6250000 0.5400000 0.5400000 
#>        Pr        Qs        Xz        Np        Dg        Hk        Wy        Jl 
#> 0.4297521 0.5244444 0.4897959 0.4687500 0.5562130 0.6015625 0.6446281 0.4177778 
#>        Fh        Zb        Eh        Be        Df        Cf        Su        Ln 
#> 0.5396825 0.6938776 0.6938776 0.6020408 0.3750000 0.6805556 0.4444444 0.4081633 
#>        Gi        Uw 
#> 0.6666667 0.6250000

Community and group-based

Map equation centrality

Bits saved by redesigning a fixed module’s codebook after silencing a node.

$$MEC(i)=-(s_m-p_i)\log_2((s_m-p_i)/s_m),\quad s_m=\sum_{j\in m}p_j+q_m$$

Meaning. A high value indicates a node whose flow matters to the description of its module: removing it saves many bits in the module’s codebook. The partition is supplied through membership (one module when NULL) and held fixed. map_convention sets whether module-exit flow is included, and the conventions can rank nodes differently.

centrality_map_equation(student_interactions)

Participation Coefficient

Participation coefficient measures how evenly a node’s ties are distributed across communities.

$$P(v) = 1 - \sum_{m} \left( \frac{k_v^{(m)}}{k_v} \right)^2, \quad k_v^{(m)} = \text{ties from } v \text{ to module } m$$

Meaning. A high value indicates ties spread across multiple communities. It requires a meaningful membership vector.

centrality_participation(student_interactions, membership = groups)

Within-Module Z Centrality

Within-module z centrality standardises a node’s within-community degree against other nodes in the same community.

$$z(v) = \frac{k_v^{(m_v)} - \mu_{m_v}}{\sigma_{m_v}}, \quad m_v = \text{community of } v$$

Meaning. A high value indicates unusually high within-module connectivity.

centrality_within_module_z(student_interactions, membership = groups)

Gateway Centrality

Gateway centrality measures inter-community brokerage weighted by centrality of communities or nodes involved.

$$g(v) = 1 - \frac{1}{k_v^2} \sum_{s} k_{vs}^2\, \gamma_{vs}^2, \quad k_{vs} = \text{ties from } v \text{ to module } s$$

Meaning. A high value indicates a structurally prominent position between communities.

centrality_gateway(student_interactions, membership = groups)

Brokerage Coordinator Centrality

Coordinator brokerage occurs when a node mediates between two nodes in its own group. It counts open directed two-paths $a \\to v \\to c$ (with no direct $a \\to c$).

$$\mathrm{Coord}(v) = |\{\, a \to v \to c : g(a) = g(v) = g(c) \,\}|$$

Meaning. A high value indicates within-group mediation under the supplied membership vector.

centrality_brokerage_coordinator(student_interactions, membership = groups)

Brokerage Itinerant Centrality

Itinerant brokerage occurs when a node mediates between two nodes in another group.

$$\mathrm{Itin}(v) = |\{\, a \to v \to c : g(a) = g(c) \ne g(v) \,\}|$$

Meaning. A high value indicates brokerage among members outside the node’s own group.

centrality_brokerage_itinerant(student_interactions, membership = groups)

Brokerage Representative Centrality

Representative brokerage occurs when a node mediates from its own group to another group.

$$\mathrm{Rep}(v) = |\{\, a \to v \to c : g(a) = g(v) \ne g(c) \,\}|$$

Meaning. A high value indicates outward brokerage from the node’s group.

centrality_brokerage_representative(student_interactions, membership = groups)

Brokerage Gatekeeper Centrality

Gatekeeper brokerage occurs when a node mediates from another group into its own group.

$$\mathrm{Gate}(v) = |\{\, a \to v \to c : g(a) \ne g(v) = g(c) \,\}|$$

Meaning. A high value indicates inward brokerage into the node’s group.

centrality_brokerage_gatekeeper(student_interactions, membership = groups)

Brokerage Liaison Centrality

Liaison brokerage occurs when a node mediates between two groups, both different from its own.

$$\mathrm{Liai}(v) = |\{\, a \to v \to c : g(a),\, g(v),\, g(c) \text{ all distinct} \,\}|$$

Meaning. A high value indicates between-group brokerage outside the node’s own group.

centrality_brokerage_liaison(student_interactions, membership = groups)

Modularity Vitality

Modularity vitality is the drop in Newman modularity when a node is deleted and the remaining nodes keep their communities.

$$V_Q(v) = Q(G, C) - Q(G - v,\; C \setminus \{v\})$$

Meaning. A positive value indicates a community hub; a negative value indicates a bridge whose removal sharpens the partition. Weighted and directed graphs use the corresponding modularity.

centrality_modularity_vitality(student_interactions, membership = groups)

Community Hub-Bridge

Community hub-bridge scores nodes that are hubs inside their community and bridges between communities.

$$CHB(v) = |C_v|\, k^{intra}_v + NNC_v\, k^{inter}_v$$

Meaning. A high value indicates a node with many intra-community links in a large community and links into several other communities.

centrality_community_hub_bridge(student_interactions, membership = groups)

Community-Based Centrality

Community-based centrality weights every link of a node by the size of the community it lands in.

$$CbC(v) = \frac{1}{N} \sum_w d_{vw} S_w$$

Meaning. A high value indicates many links into large communities.

centrality_community_based(student_interactions, membership = groups)

Comm Centrality

Comm centrality combines a node’s scaled intra-community degree with the square of its scaled inter-community degree, weighted by how outward-looking its community is.

$$CC(v) = (1 + \mu_C)\,\frac{k^{in}_v}{\max_{u \in C} k^{in}_u} R + (1 - \mu_C)\left(\frac{k^{out}_v}{\max_{u \in C} k^{out}_u} R\right)^2$$

Meaning. A high value indicates a node that is central within its community and well linked outside it. $R$ defaults to the community’s maximum intra-degree.

centrality_comm_centrality(student_interactions, membership = groups)

Community-Based Mediator

The community-based mediator score is the entropy of a node’s link distribution over communities times its share of total degree.

$$CbM(v) = H_v \frac{d_v}{\sum_u d_u}, \quad H_v = -\sum_k p_{vk} \log_2 p_{vk}$$

Meaning. A high value indicates a well-connected node whose links are spread over several communities; nodes linked to one community score 0.

centrality_community_mediator(student_interactions, membership = groups)

Argument catalogue

All measures are reachable through the single centrality() verb. These arguments control how they are computed. Measure-specific knobs are repeated in each measure’s Arguments line.

Argument	Default	Affects	What it does
type	"basic"	all	Curated tier: "basic", "extended", or "all" (ordinary-cost measures).
include	NULL	all	Add named measures, or "costly" for every costly measure.
map_flow	"unrecorded"	Map equation	Unrecorded link teleportation, or "recorded" uniform node teleportation.
map_convention	"paper"	Map equation	Include module exits as in eq11, or "infomap" to reproduce its implementation and Table1.
ninl_order	3	NINL	Nonnegative iteration count; zero returns initial degree volume.
sr_prior	0	SpectralRank	Nonnegative scalar or node vector; zero gives ordinary SR and nonzero values give diagonal-prior WSR. Ground prior stays zero.
ninl_radius	NULL	NINL	Automatic ceiling of mean path length, or explicit nonnegative integer/Inf. Disconnected automatic radius is infinite; only reachable nodes contribute.
beta_direction	"positive"	BG-index / beta power	Positive credits predecessors; negative reverses the graph and credits destinations.
mdd_lambda	0.7	MDD	Weight assigned to exhausted degree, between 0 and 1.
volume_radius	2	Volume	Closed hop neighbourhood; nonnegative integer or Inf.
diffusion_q	1	Finite-horizon diffusion	Walk multiplier in [0, 1].
diffusion_steps	3	Finite-horizon diffusion	Nonnegative integer horizon; independent of the TNA method.
ds_beta	0.1	Dynamics-sensitive	Spreading rate between zero and one.
ds_mu	1	Dynamics-sensitive	Recovery rate between zero and one; zero selects SI.
ds_steps	5	Dynamics-sensitive	Nonnegative integer time horizon.
measures	NULL	all	Character vector of specific measures to compute (overrides type).
mode	"all"	directed measures	Traversal direction: "all", "in", or "out".
normalized	FALSE	most	Divide each score by its maximum.
weighted	TRUE	weighted measures	Use edge weights when present.
directed	NULL	all	Force directed/undirected; NULL auto-detects.
loops	TRUE	degree, strength	Keep self-loops.
simplify	"sum"	multigraphs	How to combine parallel edges.
cutoff	-1	path-based	Cap path length (-1 = no cap).
invert_weights	NULL	distance/path	Treat weights as distances vs. strengths.
alpha	1	weight transform	Exponent applied when invert_weights = TRUE.
damping	0.85	PageRank	Random-walk damping factor.
personalized	NULL	PageRank	Personalization vector.
transitivity_type	"local"	transitivity	"local", "global", or weighted variants.
isolates	"nan"	transitivity	How isolates are scored.
lambda	1	diffusion	Diffusion scaling factor.
k	3	k-reach	Path-length radius.
states	NULL	percolation	Per-node percolation state vector.
decay_parameter	0.5	decay, generalized closeness	Distance-decay base.
dmnc_epsilon	1.7	DMNC	Density exponent.
katz_alpha	0.1	Katz	Attenuation factor.
hubbell_weight	0.5	Hubbell	Weight factor $w$.
membership	NULL	group-based	Community assignment vector.
digits	NULL	all	Round numeric columns.
sort_by	NULL	all	Order rows by a column name.