Abstract
Structural graph clustering is one of the fundamental problems in managing and analyzing graph data. The structural clustering algorithm SCAN is successfully used in many applications because it obtains not only clusters but also hubs and outliers. However, the results of SCAN heavily depend on two sensitive parameters, \(\epsilon \) and \(\mu \), which may result in loss of accuracy and efficiency. In this paper, we propose a novel Density Peak-based Structural Clustering Algorithm for Networks (DPSCAN). Specifically, DPSCAN clusters vertices based on the structural similarity and the structural dependency between vertices and their neighbors, without tuning parameters. Through theoretical analysis, we prove that DPSCAN can detect meaningful clusters, hubs and outliers. In addition, to improve the efficiency of DPSCAN, we propose a new index structure named DP-Index, so that each vertex needs to be visited only once. Finally, we conduct comprehensive experimental studies on real and synthetic graphs, which demonstrate that our new approach outperforms the state-of-the-art approaches.
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Acknowledgements
This work is supported by the National Key R&D Program of China (2018YFB1003404), the National Nature Science Foundation of China (61872070, U1435216, U1811261 and 61602103) and the Fundamental Research Funds for the Central Universities (N171605001).
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Wu, C., Gu, Y., Yu, G. (2019). DPSCAN: Structural Graph Clustering Based on Density Peaks. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_37
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DOI: https://doi.org/10.1007/978-3-030-18579-4_37
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