Abstract
Community structure is one of the most important topological properties of complex networks, in which the intra-group links are very dense, but the inter-group links are quite sparse. Although there exists many works with regard to community mining, few of them studied the connections between the local distance among nodes and the global community structures of networks. In this work, we have studied their connection and established a corresponding heuristics depicting such a connection between local distance and community structure. On the basis of the heuristic, we have proposed a distance-based similarity measure as well as a novel community mining algorithm DSA. The DSA has been rigorously validated and tested against several benchmark networks. The experimental results show that the DSA is able to accurately discovery the potential communities with their hierarchical structures from real-world networks.
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Li, Z., Yang, B. (2011). Mining Hierarchical Communities from Complex Networks Using Distance-Based Similarity. In: Ryżko, D., Rybiński, H., Gawrysiak, P., Kryszkiewicz, M. (eds) Emerging Intelligent Technologies in Industry. Studies in Computational Intelligence, vol 369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22732-5_16
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DOI: https://doi.org/10.1007/978-3-642-22732-5_16
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