Trust-PageRank (Sheng et al. 2020) is a damped PageRank in which a node passes its score to a neighbor in proportion to a trust value. The trust value mixes the SimRank similarity \(s\) of the two nodes with a degree ratio: $$T(i,j) = (1-k)\frac{s(i,j)}{\sum_{l \in N_j} s(j,l)} + k\,\frac{d_i}{\sum_{l \in N_j} d_l}, \qquad TPR_i = \frac{1-\alpha}{n} + \alpha \sum_{j \in N_i} T(i,j)\,TPR_j .$$
Arguments
- x
Network input accepted by
centrality.- ...
Further arguments to
centrality. The measure usestpr_alpha(damping factor, default 0.85),tpr_k(weight of the degree ratio, default 0.85),tpr_decay(SimRank decay constant, default 1),tpr_tol(relative tolerance, default1e-14) andtpr_max_iter(default 1000).
Value
A named numeric vector with one score per node, in input node order. The scores sum to one on a network with no isolated node and no undefined component.
Details
The measure is computed on the simple undirected skeleton of the
network, so direction, weights, loops and parallel edges are ignored. The
similarity recursion runs over adjacent pairs and is anchored by
triangles. Every node of a component that has edges but no triangle is
returned as NA with a cograph_undefined_measure warning, and
an isolated node scores \((1-\alpha)/n\). Both the similarity and the
score iterations stop on a relative tolerance, and an iteration that
reaches tpr_max_iter raises cograph_no_converge. The
Centrality Zoo attributes the measure to Sheng et al., Physica A
541:123262, which defines a different index. The measure is defined in
the Algorithms paper cited below.
References
Sheng, J., Zhu, J., Wang, Y., Wang, B. and Hou, Z. (2020). Identifying Influential Nodes of Complex Networks Based on Trust-Value. Algorithms, 13(11), 280. doi:10.3390/a13110280 .