Abstract
Local community detection aims at finding a community structure starting from a seed (i.e., 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 for local community detection. It estimates the similarity between vertices via the investigation on vertices’ neighborhoods, and reveals 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 GMAC algorithm.
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Ma, L., Huang, H., He, Q., Chiew, K., Wu, J., Che, Y. (2013). GMAC: A Seed-Insensitive Approach to Local Community Detection. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_26
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DOI: https://doi.org/10.1007/978-3-642-40131-2_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40130-5
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