Discovering Strong Communities with User Engagement and Tie Strength

  • Fan Zhang
  • Long Yuan
  • Ying Zhang
  • Lu Qin
  • Xuemin Lin
  • Alexander Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

In this paper, we propose and study a novel cohesive subgraph model, named (\(k\),\(s\))-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (\(k\),\(s\))-core model with \(k\)-core and \(k\)-truss theoretically and experimentally. We propose efficient algorithms to compute the (\(k\),\(s\))-core and decompose the graph by a particular sub-model \(k\)-fami. Extensive experiments show (1) our (\(k\),\(s\))-core and \(k\)-fami are effective cohesive subgraph models and (2) the (\(k\),\(s\))-core computation and \(k\)-fami decomposition are efficient on various real-life social networks.

Notes

Acknowledgments

Fan Zhang and Long Yuan are supported by Huawei YBN2017100007. Ying Zhang is supported by ARC FT170100128 and DP180103096. Lu Qin is supported by ARC DP160101513. Xuemin Lin is supported by NSFC 61672235, ARC DP170101628, DP180103096 and Huawei YBN2017100007.

References

  1. 1.
    Akbas, E., Zhao, P.: Truss-based community search: a truss-equivalence based indexing approach. PVLDB 10(11), 1298–1309 (2017)Google Scholar
  2. 2.
    Batagelj, V., Zaversnik, M.: An O(m) algorithm for cores decomposition of networks. CoRR, cs.DS/0310049 (2003)Google Scholar
  3. 3.
    Batagelj, V., Zaversnik, M.: Fast algorithms for determining (generalized) core groups in social networks. Adv. Data Anal. Classif. 5(2), 129–145 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bhawalkar, K., Kleinberg, J., Lewi, K., Roughgarden, T., Sharma, A.: Preventing unraveling in social networks: the anchored k-core problem. SIAM J. Discrete Math. 29(3), 1452–1475 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Bron, C., Kerbosch, J.: Finding all cliques of an undirected graph (algorithm 457). Commun. ACM 16(9), 575–576 (1973)CrossRefGoogle Scholar
  6. 6.
    Cohen, J.: Trusses: cohesive subgraphs for social network analysis. National Security Agency Technical Report, p. 16 (2008)Google Scholar
  7. 7.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)CrossRefGoogle Scholar
  8. 8.
    Huang, X., Cheng, H., Qin, L., Tian, W., Yu, J.X.: Querying k-truss community in large and dynamic graphs. In: SIGMOD, pp. 1311–1322 (2014)Google Scholar
  9. 9.
    Huang, X., Lakshmanan, L.V.S.: Attribute-driven community search. PVLDB 10(9), 949–960 (2017)Google Scholar
  10. 10.
    Khaouid, W., Barsky, M., Venkatesh, S., Thomo, A.: K-core decomposition of large networks on a single PC. PVLDB 9(1), 13–23 (2015)Google Scholar
  11. 11.
    Lee, P., Lakshmanan, L.V.S., Milios, E.E.: CAST: a context-aware story-teller for streaming social content. In: CIKM, pp. 789–798 (2014)Google Scholar
  12. 12.
    Luce, R.D., Perry, A.D.: A method of matrix analysis of group structure. Psychometrika 14(2), 95–116 (1949)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Rotabi, R., Kamath, K., Kleinberg, J.M., Sharma, A.: Detecting strong ties using network motifs. In: WWW, pp. 983–992 (2017)Google Scholar
  14. 14.
    Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Seidman, S.B., Foster, B.L.: A graph-theoretic generalization of the clique concept. J. Math. Sociol. 6(1), 139–154 (1978)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shao, Y., Chen, L., Cui, B.: Efficient cohesive subgraphs detection in parallel. In: SIGMOD, pp. 613–624 (2014)Google Scholar
  17. 17.
    Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J.: Structural diversity in social contagion. PNAS 109(16), 5962–5966 (2012)CrossRefGoogle Scholar
  18. 18.
    Wang, J., Cheng, J.: Truss decomposition in massive networks. PVLDB 5(9), 812–823 (2012)Google Scholar
  19. 19.
    Zhang, F., Zhang, W., Zhang, Y., Qin, L., Lin, X.: OLAK: an efficient algorithm to prevent unraveling in social networks. PVLDB 10(6), 649–660 (2017)Google Scholar
  20. 20.
    Zhang, F., Zhang, Y., Qin, L., Zhang, W., Lin, X.: Finding critical users for social network engagement: the collapsed k-core problem. In: AAAI, pp. 245–251 (2017)Google Scholar
  21. 21.
    Zhang, F., Zhang, Y., Qin, L., Zhang, W., Lin, X.: When engagement meets similarity: efficient (k, r)-core computation on social networks. PVLDB 10(10), 998–1009 (2017)Google Scholar
  22. 22.
    Zhang, Y., Yu, J.X., Zhang, Y., Qin, L.: A fast order-based approach for core maintenance. In: ICDE, pp. 337–348 (2017)Google Scholar
  23. 23.
    Zhao, F., Tung, A.K.H.: Large scale cohesive subgraphs discovery for social network visual analysis. PVLDB 6(2), 85–96 (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Fan Zhang
    • 1
  • Long Yuan
    • 1
  • Ying Zhang
    • 2
  • Lu Qin
    • 2
  • Xuemin Lin
    • 1
  • Alexander Zhou
    • 3
  1. 1.University of New South WalesSydneyAustralia
  2. 2.Centre for AIUniversity of Technology SydneySydneyAustralia
  3. 3.University of QueenslandBrisbaneAustralia

Personalised recommendations