Abstract
Communities is a significant pattern of the Web. A community is a group of pages related to a common topic. Web communities are able to be characterized by dense bipartite subgraphs. Each community almost surely contains at least one core. A core is a complete bipartite graph (CBG). Focusing on the issues of extracting such community cores from the Web, in this paper we propose an effective C&C algorithm based on combination and consolidation to extract all embedded cores in web graphs. Experiments on real and large data collections demonstrate that the proposed algorithm C&C is efficient and effective for the community core extraction because: 1) all the largest emerging cores can be identified; 2) identifying all the embedded cores with different sizes only requires one-pass execution of C&C; 3) the extraction process needs no user-determined parameters in C&C.
This work was partially supported by NSFC under grant No. 60873180, and by the start-up funding (#1600-893313) for newly appointed academic staff of Dalian University of Technology, China.
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Zhang, X., Li, Y., Liang, W. (2010). C&C: An Effective Algorithm for Extracting Web Community Cores. In: Yoshikawa, M., Meng, X., Yumoto, T., Ma, Q., Sun, L., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 6193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14589-6_32
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DOI: https://doi.org/10.1007/978-3-642-14589-6_32
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