Abstract
In the real world, many systems can be represented as a network, in which the nodes denote the objects of interest and the edges describe the relations between them, such as telecommunication networks, power grid networks, and email communication networks. These complex networks have been revealed to possess many common statistical properties such as scale-free nature and small-world property. In addition, modularity or community structure is another important characteristic of complex networks. Identifying modular structure can help us understand the function of networks. In this paper, we introduce a method based on information-theoretic clustering for finding communities/modules in complex networks. This method is robust to the feature representation of networks. Moreover, unlike most existing algorithms, this method does not need to search the number of communities in a network and can determine it automatically. We apply this method to several well-studied networks including a large-scale email communication network and the computational results demonstrate its effectiveness.
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Huang, Y., Wang, G. (2010). Structure Analysis of Email Networks by Information-Theoretic Clustering. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_52
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DOI: https://doi.org/10.1007/978-3-642-13318-3_52
Publisher Name: Springer, Berlin, Heidelberg
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