Abstract
Graph partitioning, or network clustering, is an essential research problem in many areas. Current approaches, however, have difficulty splitting two clusters that are densely connected by one or more “hub” vertices. Further, traditional methods are less able to deal with very confused structures. In this paper we propose a novel similarity-based definition of the quality of a partitioning of a graph. Through theoretical analysis and experimental results we demonstrate that the proposed definition largely overcomes the “hub” problem and outperforms existing approaches on complicated graphs. In addition, we show that this definition can be used with fast agglomerative algorithms to find communities in very large networks.
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Feng, Z., Xu, X., Yuruk, N., Schweiger, T.A.J. (2007). A Novel Similarity-Based Modularity Function for Graph Partitioning. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_36
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DOI: https://doi.org/10.1007/978-3-540-74553-2_36
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