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CCF Conference on Big Data

BigData 2019: Big Data pp 194–208Cite as

Distributed Graph Perturbation Algorithm on Social Networks with Reachability Preservation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1120))

Abstract

With the rapid development of social networks, the current scale of graph data continues to increase, and the performance of anonymous social network methods is limited. Node reachability query is essential in directed graphs, which can reflect the relationship between nodes and the direction of information dissemination. Aiming at the problem of the reachability of nodes between directed social network privacy technologies, this paper proposes a reachability preserving distribution perturbation (RPDP) algorithm, which is based on the distributed graph processing system GraphX. This algorithm first generates a Random Neighborhood Table (RNT) composed of four tuples for the nodes and then uses the message transmission of GraphX and “probe” mechanism. The proposed algorithm improves the disposal efficiency of the large-scale social network while maintaining the reachability of the nodes. Experiments based on the real social network data show that the proposed algorithm can keep the node reachability and deal with large-scale social network efficiently while protecting the character of the graph structure.

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Acknowledgments

This work is partially supported by Natural Science Foundation of China (No. 61562065). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

Author information

Authors and Affiliations

  1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China

    Xiaolin Zhang, Jian Li & Jiao Liu

  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200000, China

    Xiaoyu He

Authors
  1. Xiaolin Zhang
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  2. Jian Li
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  3. Xiaoyu He
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  4. Jiao Liu
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Corresponding author

Correspondence to Xiaolin Zhang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Hai Jin

  2. East China Normal University, Shanghai, China

    Xuemin Lin

  3. Chinese Academy of Sciences, Beijing, China

    Xueqi Cheng

  4. Huazhong University of Science and Technology, Wuhan, China

    Xuanhua Shi

  5. National University of Defense Technology, Changsha, China

    Nong Xiao

  6. Nanjing University, Nanjing, China

    Yihua Huang

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© 2019 Springer Nature Singapore Pte Ltd.

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Zhang, X., Li, J., He, X., Liu, J. (2019). Distributed Graph Perturbation Algorithm on Social Networks with Reachability Preservation. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_14

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  • DOI: https://doi.org/10.1007/978-981-15-1899-7_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1898-0

  • Online ISBN: 978-981-15-1899-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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