Abstract
In this paper, we introduce a new graph clustering algorithm, called Dcut. The basic idea is to envision the graph clustering as a local density-cut problem. To identify meaningful communities in a graph, a density-connected tree is first constructed in a local fashion. Building upon the local intuitive density-connected tree, Dcut allows partitioning a graph into multiple densely tight-knit clusters effectively and efficiently. We have demonstrated that our method has several attractive benefits: (a) Dcut provides an intuitive criterion to evaluate the goodness of a graph clustering in a more precise way; (b) Building upon the density-connected tree, Dcut allows identifying high-quality clusters; (c) The density-connected tree also provides a connectivity map of vertices in a graph from a local density perspective. We systematically evaluate our new clustering approach on synthetic and real-world data sets to demonstrate its good performance.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61403062, 41601025, 61433014), Science-Technology Foundation for Young Scientist of SiChuan Province (2016JQ0007), State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2017490211), National key research and development program (2016YFB0502300).
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Shao, J., Yang, Q., Zhang, Z., Liu, J., Kramer, S. (2018). Graph Clustering with Local Density-Cut. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_13
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