Complex Systems

Causal Paths in Temporal Networks of Face-to-Face Human Interactions Download PDF

Agostino Funel
ENEA - Energy Technologies Department, ICT HPC Lab
Via E. Fermi, 1
80055 Portici (Naples), Italy


In a temporal network, causal paths are characterized by the fact that links from a source to a target must respect the chronological order. In this paper we study the causal paths structure in temporal networks of face-to-face human interactions in different social contexts. In a static network, paths are transitive; that is, the existence of a link from a to b and from b to c implies the existence of a path from a to c via b. In a temporal network, the chronological constraint introduces time correlations that affect transitivity. A probabilistic model based on higher-order Markov chains shows that correlations that can invalidate transitivity are present only when the time gap between consecutive events is larger than the average value and are negligible below such a value. The comparison between the densities of the temporal and static accessibility matrices shows that the static representation can be used with good approximation. Moreover, we quantify the extent of the causally connected region of the networks over time.

Keywords: temporal networks; human interactions; causal paths; Markov chains; probabilistic models

Cite this publication as:
A. Funel, “Causal Paths in Temporal Networks of Face-to-Face Human Interactions,” Complex Systems, 30(1), 2021 pp. 33–46.