Efficient computing of radius-bounded κ-cores

Publication Type:
Conference Proceeding
Citation:
Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018, 2018, pp. 233 - 244
Issue Date:
2018-10-24
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[2018 ICDE] Efficient computing of radius-bounded κ-cores.pdfAccepted Manuscript version1.13 MB
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© 2018 IEEE. Driven by real-life applications in geo-social networks, in this paper, we investigate the problem of computing the radius-bounded k-cores (RB-k-cores) that aims to find cohesive subgraphs satisfying both social and spatial constraints on large geo-social networks. In particular, we use k-core to ensure the social cohesiveness and we use a radius-bounded circle to restrict the locations of users in a RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating circle-based paradigm is further enhanced with several pruning techniques to achieve better efficiency. The experimental studies conducted on both real and synthetic datasets demonstrate that our proposed rotating-circle-based algorithms can compute all RB-k-cores very efficiently. Moreover, it can also be used to compute the minimum-circle-bounded k-core and significantly outperforms the existing techniques for computing the minimum circle-bounded k-core.
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