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Toward seed-insensitive solutions to local community detection

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Abstract

Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.

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Notes

  1. A conference version of GMAC can be found in (Ma et al. 2013).

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Acknowledgements

This work was supported by the National Key Technologies R&D Program of China under Grant No. 2012BAH94F01.

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, People’s Republic of China

    Lianhang Ma, Qinming He & Zhenguang Liu

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Hao Huang

  3. Provident Technology Pte. Ltd., Singapore, Singapore

    Kevin Chiew

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  1. Lianhang Ma
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  2. Hao Huang
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  3. Qinming He
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  4. Kevin Chiew
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  5. Zhenguang Liu
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Correspondence to Qinming He.

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Ma, L., Huang, H., He, Q. et al. Toward seed-insensitive solutions to local community detection. J Intell Inf Syst 43, 183–203 (2014). https://doi.org/10.1007/s10844-014-0315-6

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