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Summarise a Heterogeneous Transition Network

Source: R/summary_htna.R
summary.htna.Rd

Prints a per-actor summary of an htna network: which nodes belong to which actor type and how the non-zero edges distribute across the actor partition. The result is also returned invisibly as a list so callers can inspect the structured summary programmatically.

Usage

# S3 method for class 'htna'
summary(object, max_nodes = 12L, ...)

# S3 method for class 'htna_group'
summary(object, max_nodes = 12L, ...)

Arguments

object

An htna network from build_htna() or an htna_group.

max_nodes

Integer. Maximum number of nodes to list per actor type before truncating with an ellipsis. Default 12.

...

Forwarded for compatibility; currently unused.

Value

Invisibly, a list with components:

  • actors - data frame with one row per actor type (actor, n_nodes, nodes).

  • edges_by_actor - integer matrix of non-zero edge counts, rows are source actor, columns are target actor.

  • network_metrics - standard network-level descriptive metrics from the Nestimate engine (node/edge counts, density, distance, strength, degree centralization, and reciprocity).

  • n_nodes, n_edges, n_sessions, n_timesteps, method.

Examples

data(human_ai)
net <- build_htna(human_ai, actor_type = "actor_type")
#> Warning: A network with one long sequence is not recommended and can't be validated using bootstrap and other confirmatory testings.
#> Metadata aggregated per session: ties resolved by first occurrence in 'session_date' (1 sessions), 'cluster' (42 sessions), 'actor_type' (24 sessions)
summary(net)
#> <htna network>
#>   Method:    relative
#>   Sessions:  429   (max 287 timesteps)
#>   Nodes:     12
#>   Edges:     135 / 144 (non-zero)
#> 
#> Actor types (2):
#>   Human (6 nodes):  Check, Frustrate, Inquire, Refine, Request, Specify
#>   AI    (6 nodes):  Ask, Delegate, Execute, Plan, Repair, Report
#> 
#> Edge counts by actor (rows = source, cols = target):
#>           Human    AI
#>   Human      35    36
#>   AI         36    28
#> 
#> Network metrics:
#>                       metric        value
#>                   Node Count  12.00000000
#>                   Edge Count 135.00000000
#>              Network Density   1.00000000
#>                Mean Distance   0.02743124
#>            Mean Out-Strength   1.00000000
#>              SD Out-Strength   0.63085045
#>             Mean In-Strength   1.00000000
#>               SD In-Strength   0.00000000
#>              Mean Out-Degree  11.25000000
#>                SD Out-Degree   0.96530730
#>  Centralization (Out-Degree)   0.05785124
#>   Centralization (In-Degree)   0.05785124
#>                  Reciprocity   0.96000000