Efficiently enumerating all maximal cliques with bit-parallelism. (April 2018)

• Record Type: Journal Article
• Authors: San Segundo, Pablo; Artieda, Jorge; Strash, Darren
• Is Part Of: Computers & operations research. Volume 92(2018)
• Page Start: 37
• Page End: 46
• Publication Date: 2018-04
• Subjects: Maximal clique -- Bitstring -- Branch-and-bound -- Subgraph enumeration -- Combinatorial optimization; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2017.12.006
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: New state-of-the-art exact clique enumeration algorithm for small and middle-size graphs. Efficient pivoting rule combined with a bit-parallel encoding. Improved preconditioning: nodes are sorted according to degree. Abstract: The maximal clique enumeration (MCE) problem has numerous applications in biology, chemistry, sociology, and graph modeling. Though this problem is well studied, most current research focuses on finding solutions in large sparse graphs or very dense graphs, while sacrificing efficiency on the most difficult medium-density benchmark instances that are representative of data sets often encountered in practice. We show that techniques that have been successfully applied to the maximum clique problem give significant speed gains over the state-of-the-art MCE algorithms on these instances. Specifically, we show that a simple greedy pivot selection based on a fixed maximum-degree first ordering of vertices, when combined with bit-parallelism, performs consistently better than the theoretical worst-case optimal pivoting of the state-of-the-art algorithms of Tomita et al. [Theoretical Computer Science, 2006] and Naudé [Theoretical Computer Science, 2016]. Experiments show that our algorithm is faster than the worst-case optimal algorithm of Tomita et al. on 60 out of 74 standard structured and random benchmark instances: we solve 48 instances 1.2 to 2.2 times faster, and solve the remaining 12 instances 3.6 to 47.6 times faster. We also see consistent speed improvements over the algorithm of Naudé: solving 61 instances 1.2 to 2.4 times faster. To the best of our knowledge, we are the first to achieve such speed-ups compared to these state-of-the-art algorithms on these standard benchmarks.