Adapting the Bron–Kerbosch algorithm for enumerating maximal cliques in temporal graphs
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Dynamics of interactions play an increasingly important role in the analysis of complex networks. A modeling framework to capture this is temporal graphs which consist of a set of vertices (entities in the network) and a set of time-stamped binary interactions between the vertices. We focus on enumerating Δ-cliques, an extension of the concept of cliques to temporal graphs: for a given time period Δ, a Δ-clique in a temporal graph is a set of vertices and a time interval such that all vertices interact with each other at least after every Δ time steps within the time interval. Viard, Latapy, and Magnien (ASONAM 2015, TCS 2016) proposed a greedy algorithm for enumerating all maximal Δ-cliques in temporal graphs. In contrast to this approach, we adapt the Bron–Kerbosch algorithm—an efficient, recursive backtracking algorithm which enumerates all maximal cliques in static graphs—to the temporal setting. We obtain encouraging results both in theory (concerning worst-case running time analysis based on the parameter “Δ-slice degeneracy” of the underlying graph) as well as in practice with experiments on real-world data. The latter culminates in an improvement for most interesting Δ-values concerning running time in comparison with the algorithm of Viard, Latapy, and Magnien.
KeywordsData science Time-varying networks Δ-clique Community detection Degeneracy Fixed-parameter tractability Experimental analysis
Anne-Sophie Himmel, Hendrik Molter and Manuel Sorge were partially supported by DFG, Project DAPA (NI 369/12). Manuel Sorge gratefully acknowledges support by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant Agreement Number 631163.11 and by the Israel Science Foundation (Grant No. 551145/14). We are grateful to two anonymous SNAM reviewers whose feedback helped to significantly improve the presentation and to eliminate some bugs and inconsistencies.
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