Abstract
The k-truss is a type of cohesive subgraphs proposed for the analysis of massive network. Existing in-memory algorithms for computing k-truss are inefficient for searching and parallel. We propose a novel traversal algorithm for truss decomposition: it effectively reduces computation complexity, we fully exploit the parallelism thanks to the optimization, and overlap IO and computation for a better performance. Our experiments on real datasets verify that it is 2x–5x faster than the exiting fastest in-memory algorithm.
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Acknowledgments
We are grateful to our anonymous reviewers for their suggestions to improve this paper. This work is supported by the National High-Tech Research and Development Projects (863) and the National Natural Science Foundation of China under Grant Nos. 2015AA015305, 61232003, 61332003, 61202121.
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Xing, Y., Xiao, N., Lu, Y., Li, R., Yu, S., Gao, S. (2017). Fast Truss Decomposition in Memory. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10658. Springer, Cham. https://doi.org/10.1007/978-3-319-72395-2_64
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DOI: https://doi.org/10.1007/978-3-319-72395-2_64
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