Abstract
Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that network often exhibit hierarchical organization, where vertices divide into groups that further subdivided into groups of groups, and so forth over multiple scales. In this paper, we introduce a novel algorithm that searches for the hierarchical structure. The method iteratively combines the similar communities with the elaborate design of community similarity and combination threshold. The experiments on artificial and real networks show that the method is able to obtain reasonable hierarchical structure solutions.
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Shi, C., Zhang, J., Shi, L., Cai, Y., Wu, B. (2010). A Novel Algorithm for Hierarchical Community Structure Detection in Complex Networks. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_53
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DOI: https://doi.org/10.1007/978-3-642-17316-5_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17315-8
Online ISBN: 978-3-642-17316-5
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