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Finding maximal cliques in massive networks
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| Venue: | ACM Trans. Database Syst |
| Citations: | 13 - 5 self |
BibTeX
@ARTICLE{Cheng_findingmaximal,
author = {James Cheng and Yiping Ke},
title = {Finding maximal cliques in massive networks},
journal = {ACM Trans. Database Syst},
year = {},
pages = {10--1145}
}
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Abstract
Maximal clique enumeration is a fundamental problem in graph theory and has important applications in many areas such as social network analysis and bioinformatics. The problem is extensively studied; however, the best existing algorithms require memory space linear in the size of the input graph. This has become a serious concern in view of the massive volume of today’s fast-growing networks. We propose a general framework for designing external-memory algorithms for maximal clique enumeration in large graphs. The general framework enables maximal clique enumeration to be processed recursively in small subgraphs of the input graph, thus allowing in-memory computation of maximal cliques without the costly random disk access. We prove that the set of cliques obtained by the recursive local computation is both correct (i.e., globally maximal) and complete. The subgraph to be processed each time is defined based on a set of base vertices that can be flexibly chosen to achieve different purposes. We discuss the selection of the base vertices to fully utilize the available memory in order to minimize I/O cost in static graphs, and for update maintenance in dynamic graphs. We also apply our framework to design an external-memory algorithm for maximum clique computation in a large graph.
Keyphrases
maximal clique maximal clique enumeration massive network input graph general framework large graph base vertex external-memory algorithm fast-growing network memory space linear static graph maximum clique computation important application small subgraphs massive volume different purpose serious concern graph theory many area social network analysis available memory recursive local computation update maintenance in-memory computation dynamic graph costly random disk access fundamental problem
