Direction Recovery in Undirected Social Networks Based on Community Structure and Popularity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Directionality is a significant property of social networks, which enables us to improve our analytical tasks and have a deeper understanding about social networks. Unfortunately, the potential directionality is hidden in undirected social networks. The previous studies on recovering directionality in undirected social networks mostly focus on the microscopic patterns discovered in the existing directed social networks. In this paper, we attempt to recover the directionality based on the macroscopic community structure. To this end, a variant of the existing modularity model, called behavioural modularity, is designed for discovering community membership of nodes. Assuming that members in the same community have higher behavioural similarity, we introduce the concept of the intra-community popularity, and then estimate directionality of undirected ties based on the community structure and the intra-community popularity. Accordingly, we propose a novel Community and Popularity based Direction Recovering (CPDR) approach to recover the directionality of undirected social networks. Experimental results conducted on three real-world social networks have confirmed the effectiveness of the proposed approach on direction recovery.

Keywords

Direction recovery Popularity Community detection Behavioural similarity 

Notes

Acknowledgments

This work was supported by NSFC (61502543) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).

References

  1. 1.
    Yin, H., Benson, A.R., Leskovec, J., Gleich, D.F.: Local higher-order graph clustering. In: KDD, pp. 555–564 (2017)Google Scholar
  2. 2.
    Ripeanu, M., Foster, I.T., Iamnitchi, A.: Mapping the Gnutella network: properties of large-scale peer-to-peer systems and implications for system design. CoRR cs.DC/0209028 (2002)CrossRefGoogle Scholar
  3. 3.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: WWW, pp. 641–650 (2010)Google Scholar
  4. 4.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370 (2010)Google Scholar
  5. 5.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  6. 6.
    Liu, L., Xu, L., Wangy, Z., Chen, E.: Community detection based on structure and content: a content propagation perspective. In: ICDM, pp. 271–280 (2015)Google Scholar
  7. 7.
    Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008)CrossRefGoogle Scholar
  8. 8.
    Ma, H., Zhou, T.C., Lyu, M.R., King, I.: Improving recommender systems by incorporating social contextual information. ACM Trans. Inf. Syst. 29(2), 9 (2011)CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Wang, C., Wang, J., Yu, J.X., Chen, J., Wang, C.: Inferring directions of undirected social ties. IEEE Trans. Knowl. Data Eng. 28(12), 3276–3292 (2016)CrossRefGoogle Scholar
  10. 10.
    Peng, X.-R., Huang, L., Wang, C.-D.: A hybrid approach for recovering information propagational direction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 357–367. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70139-4_36CrossRefGoogle Scholar
  11. 11.
    Newman, M.E.: Modularity and community structure in networks. Proc. Nat. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  12. 12.
    Yang, T., Jin, R., Chi, Y., Zhu, S.: Combining link and content for community detection: a discriminative approach. In: KDD, pp. 927–936. ACM (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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