Listing All Maximal k-Plexes in Temporal Graphs

by   Matthias Bentert, et al.

Social networks evolve over time, that is, new contacts appear and old contacts may disappear. They can be modeled as temporal graphs where interactions between vertices (people) are represented by time-stamped edges. One of the most fundamental problems in social network analysis is community detection, and one of the most basic primitives to model a community is a clique. Addressing the problem of finding communities in temporal networks, Viard et al. [ASONAM 2015] introduced delta-cliques as a natural temporal version of cliques. Himmel et al. [ASONAM 2016] showed how to adapt the well-known Bron-Kerbosch algorithm to enumerate delta-cliques. We continue this work and improve and extend this algorithm to enumerate temporal k-plexes. We define a delta-k-plex as a set of vertices with a lifetime, where during the lifetime each vertex has an edge to all but at most k-1 vertices at least once within any consecutive delta+1 time steps. We develop the algorithm delta-k-BronKerbosch for enumerating all maximal delta-k-plexes and perform experiments on real-world networks that demonstrate the feasibility of our approach. In particular, for the special case of delta-1-plexes (that is, delta-cliques), we observe that delta-k-BronKerbosch is even faster than the previous algorithm by Himmel et al. at enumerating delta-cliques.


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