Abstract
How can we efficiently find clusters (a.k.a. communities) included in a graph with millions or even billions of edges? Density-based graph clustering SCAN is one of the fundamental graph clustering algorithms that can find densely connected nodes as clusters. Although SCAN is used in many applications due to its effectiveness, it is computationally expensive to apply SCAN to large-scale graphs since SCAN needs to compute all nodes and edges. In this paper, we propose a novel density-based graph clustering algorithm named ScaleSCAN for tackling this problem on a multicore CPU. Towards the problem, ScaleSCAN integrates efficient node pruning methods and parallel computation schemes on the multicore CPU for avoiding the exhaustive nodes and edges computations. As a result, ScaleSCAN detects exactly same clusters as those of SCAN with much shorter computation time. Extensive experiments on both real-world and synthetic graphs demonstrate that the performance superiority of ScaleSCAN over the state-of-the-art methods.
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We opened our source codes of ScaleSCAN on our website.
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Acknowledgement
This work was supported by JSPS KAKENHI Early-Career Scientists Grant Number JP18K18057, JST ACT-I, and Interdisciplinary Computational Science Program in CCS, University of Tsukuba.
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Shiokawa, H., Takahashi, T., Kitagawa, H. (2018). ScaleSCAN: Scalable Density-Based Graph Clustering. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_2
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