Abstract
Efficient enumeration of all maximal cliques in a given graph has many applications in the filed of Graph Theory, Social Network Analysis, Bioinformatics and etc. Recent researches indicate that many networks in our world are complex networks involving massive data. Being as the complete sub-graph, a maximal clique can represent a group of friends who all hang around together. It can also be used to find common sub-topologies in a set of protein structures. However, the large scale of real networks and the exponentially increasing computation time of the clique enumeration problem make most of the existing algorithms unsuitable in the real-world scenarios. Therefore, we present a parallel algorithm Peamc (Parallel Enumeration of All Maximal Cliques) which exploits several new and effective techniques to enumerate all maximal cliques in large-scale complex networks. Experimental results on true-life networks with up to 20 million vertices and 50 million edges show that Peamc can find all the maximal cliques with high efficiency and scalability.
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© 2009 Springer-Verlag Berlin Heidelberg
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Du, N., Wu, B., Xu, L., Wang, B., Xin, P. (2009). Parallel Algorithm for Enumerating Maximal Cliques in Complex Network. In: Zighed, D.A., Tsumoto, S., Ras, Z.W., Hacid, H. (eds) Mining Complex Data. Studies in Computational Intelligence, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88067-7_12
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DOI: https://doi.org/10.1007/978-3-540-88067-7_12
Publisher Name: Springer, Berlin, Heidelberg
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