Path lengths, correlations, and centrality in temporal networks

TL;DR

This paper investigates the properties of temporal paths in dynamic networks, revealing how temporal distances and centralities differ from static network measures through empirical studies of human communication and air transport.

Contribution

It introduces a definition and algorithm for average temporal distance, analyzing how temporal paths differ from static network expectations in real-world networks.

Findings

Temporal distances correlate with static distances but show large variability.

Nodes close in static networks may have slow or no temporal paths.

Event sequence heterogeneity influences temporal path lengths differently across networks.

Abstract

In temporal networks, where nodes interact via sequences of temporary events, information or resources can only flow through paths that follow the time-ordering of events. Such temporal paths play a crucial role in dynamic processes. However, since networks have so far been usually considered static or quasi-static, the properties of temporal paths are not yet well understood. Building on a definition and algorithmic implementation of the average temporal distance between nodes, we study temporal paths in empirical networks of human communication and air transport. Although temporal distances correlate with static graph distances, there is a large spread, and nodes that appear close from the static network view may be connected via slow paths or not at all. Differences between static and temporal properties are further highlighted in studies of the temporal closeness centrality. In addition, correlations and heterogeneities in the underlying event sequences affect temporal path lengths, increasing temporal distances in communication networks and decreasing them in the air transport network.