On fast enumeration of maximal cliques in large graphs

Maximal Clique Enumeration (MCE) is a fundamental and challenging problem in graph theory and various network applications. Numerous algorithms have been proposed in the past decades, however, only a few of them focus on improving the practical efficiency in large graphs. To this end, we propose an efficient algorithm called FACEN based on the Bron-Kerbosch framework. To optimize the memory and time consumption, we apply a hybrid data structure with adjacency list and partial adjacency matrix, and introduce a dynamic pivot selection rule based on the degeneracy order. FACEN is evaluated on a total of 64 benchmark instances from various sources. Computational results indicate that the proposed algorithm is highly competitive with the current leading MCE methods. In particular, our algorithm is able to enumerate all maximal cliques on the tested real-world social networks with millions of vertices and edges. For very large graphs, we provide an additional experiment for solving the MCE variant with lower bound, and investigate the benefits of FACEN.

Automated summary

Maximal Clique Enumeration (MCE) is a fundamental and challenging problem in graph theory and various network applications. A new efficient algorithm FACEN based on the Bron-Kerbosch framework is proposed, which shows competitive performance with leading MCE methods and can handle very large graphs efficiently.