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I/O efficient k-truss community search in massive graphs

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Abstract

Community detection that discovers all densely connected communities in a network has been studied a lot. In this paper, we study online community search for query-dependent communities, which is a different but practically useful task. Given a query vertex in a graph, the problem is to find meaningful communities that the vertex belongs to in an online manner. We propose a community model based on the k-truss concept, which brings nice structural and computational properties. We design a compact and elegant index structure which supports the efficient search of k-truss communities with a linear cost with respect to the community size. We also investigate the k-truss community search problem in a dynamic graph setting with frequent insertions and deletions of graph vertices and edges. In addition, to support k-truss community search over massive graphs which cannot entirely fit in main memory, we propose I/O-efficient algorithms for query processing under the semi-external model. Extensive experiments on massive real-world networks demonstrate the effectiveness of our k-truss community model, the efficiency, and the scalability of our in-memory and semi-external community search algorithms.

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Notes

  1. http://www.wise2012.cs.ucy.ac.cy/challenge.html

  2. http://law.di.unimi.it/datasets.php

  3. http://snap.stanford.edu/data/index.html.

  4. http://pywebgraph.sourceforge.net/

  5. http://dblp.uni-trier.de/xml/

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Acknowledgements

The work was supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China [Project No.: CUHK 14205617], [Project No.: CUHK 14205618], and [Project No.: HKBU 22200320], and NSFC Grant Nos. U1936205 and 61702435.

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  1. The Chinese University of Hong Kong, Hong Kong, China

    Yuli Jiang & Hong Cheng

  2. Hong Kong Baptist University, Hong Kong, China

    Xin Huang

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  1. Yuli Jiang
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  2. Xin Huang
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  3. Hong Cheng
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Correspondence to Xin Huang.

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Jiang, Y., Huang, X. & Cheng, H. I/O efficient k-truss community search in massive graphs. The VLDB Journal 30, 713–738 (2021). https://doi.org/10.1007/s00778-020-00649-y

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