A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network

Authors:
Nan Du;Bin Wu;Liutong Xu;Bai Wang;Xin Pei
Affiliations:
Beijing University of Posts and Telecommunications, China;Beijing University of Posts and Telecommunications, China;Beijing University of Posts and Telecommunications, China;Beijing University of Posts and Telecommunications, China;Beijing University of Posts and Telecommunications, China
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

Efficient enumeration of all maximal cliques in a given graph has many applications in Graph Theory, Data Mining and Bioinformatics. However, the exponentially increasing computation time of this problem confines the scale of the graph. Meanwhile, recent researches show that many networks in our world are complex networks involving massive data. To solve the maximal clique problem in the real-world scenarios, this paper presents a parallel algorithm Peamc (Parallel Enumeration of All Maximal Cliques) which exploits several new and effective techniques to enumerate all maximal cliques in a complex network. Furthermore, we provide a performance study on a true-life call graph with up to 2,423,807 vertices and 5,317,183 edges. The experimental results show that Peamc can find all the maximal cliques in a complex network with high efficiency and scalability.