path_centrality: Closeness and betweenness on time-respecting paths

In Dynet: Tidy Temporal Network Analysis

Closeness and betweenness on time-respecting paths

Description

Centrality computed from the time-respecting paths that paths() finds, taken across the whole observation period (or the start-to-end window). A path may only continue along a tie that is available after it arrives, so these values cannot be inflated by ties that occur in the wrong order, as a flattened network is. The result is one value per vertex, not a series: for centrality that changes from window to window, use centrality_series(); for the number of vertices a vertex can reach, use reachability().

Usage

path_centrality(
  dn,
  measure = "closeness",
  sessions = c("bounded", "collapse", "separate"),
  start = NULL,
  end = NULL,
  traversal_time = 0,
  plot = FALSE
)

Arguments

dn	A temporal network from dynet().
measure	One or both of "closeness" (the default) and "betweenness". Any other name raises dynet_unknown_measure.
sessions	How to treat sessions: "bounded" (the default) keeps paths inside a session, "collapse" ignores sessions, "separate" reports each session on its own rows. "separate" on a network built without a session column raises dynet_no_sessions.
start, end	Inclusive path-traversal bounds. Default to the observed range. A network built from dates may be addressed with dates.
traversal_time	Nonnegative duration charged for every hop, in the network's time unit; 0 by default. A calendar network also accepts a scalar difftime.
plot	Whether to draw the result as well as return it. Drawing is a side effect in the manner of graphics::hist(): the verb still returns its tidy table, invisibly when it has drawn.

Details

Betweenness is the raw dependency sum over reachable forward ordered pairs. For each source-target pair, its unit dependency is divided equally over every canonical shortest-foremost journey, and an internal vertex receives the fraction of those journeys that contain it. Sources and targets receive no endpoint credit. This ordered-pair convention also applies to undirected contacts because temporal reach is generally asymmetric. The result is not normalised; its fixed range is ⁠[0, (n - 1) * (n - 2)]⁠.

Closeness is inverse mean forward latency over reachable vertices: if R_s is the set of reachable vertices other than source s,

C(s) = |R_s| / \sum_{z \in R_s} (a_z - o_s),

where a_z is the foremost arrival time and o_s is the source's resolved origin: the traversal window's lower bound, or – when vertex activity was declared – the source's first presence inside that window. Every reachable endpoint is included once, regardless of how many optimal paths reach it. A source with no reachable nonself endpoints has value zero. If all reachable endpoints have zero latency, the value is Inf; zero-latency endpoints remain in the numerator when mixed with positive latencies. The measure therefore has inverse-time units, is invariant to translating the time axis, and scales inversely when time is rescaled.

Both measures use paths() traversal semantics: nondecreasing times, unlimited waiting, half-open interval spells, and a separate exact timestamp rule for point events. Positive traversal_time requires an interval traversal to finish within continuous pair activity; a point event triggers at its timestamp and reaches its endpoint after that duration. start and end bound every measure. In separate-session output, a session outside a one-sided bound contributes zero rows.

Declared vertex activity gates the exact source anchor and every hop. Waiting after a valid anchor may cross inactivity; interval traversal requires both endpoints through completion, while a point trigger requires the receiver again after any traversal delay. Fixed node rows and full-network denominators are retained.

Value

A node-level dynet_metric: a tidy data frame with one row per vertex and measure, columns node, measure and value, preceded by session under sessions = "separate". There is no time column. A single-measure result stores its mathematical choices as direct attributes; a two-measure result stores named records under measure_metadata.

Conditions

Errors: dynet_unknown_measure (a measure other than "closeness" or "betweenness"), dynet_no_sessions (sessions = "separate" without a session column), dynet_outside_observation (the requested range misses observed support; it also carries dynet_bad_input), and dynet_bad_input for every other broken contract – dn not a dynet, a malformed measure, an out-of-range start, end or traversal_time.

References

Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105.

Tang, J., Musolesi, M., Mascolo, C., Latora, V., & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of SNS '10.

Buss, S., Molter, H., Niedermeier, R., & Rymar, M. (2024). Algorithmic aspects of temporal betweenness. Network Science, 12(2), 160-188.

Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., & Latora, V. (2013). Graph metrics for temporal networks. In Temporal Networks (pp. 15-40). Springer.

See Also

paths(), reachability(), centrality_series().

Examples

# Every ordered pair is searched, so the cost grows steeply with the
# vertex count; a subgraph keeps the example quick.
dn <- dynet(school_contacts)
few <- induce_subgraph(dn, nodes = c("Ana", "Ben", "Cara", "Dan", "Eve",
                                     "Finn", "Gita", "Hugo"))
path_centrality(few)
path_centrality(few, measure = c("closeness", "betweenness"),
                start = 0, end = 10)

Dynet documentation built on Oct. 7, 2026, 5:08 p.m.