Abstract
Local community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 61702015, U1936104) and The Fundamental Research Funds for the Central Universities 2020RC25.
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Liu, J., Shao, Y., Su, S. (2020). Multiple Local Community Detection via High-Quality Seed Identification. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_4
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DOI: https://doi.org/10.1007/978-3-030-60259-8_4
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