Skip to main content
SpringerLink
Log in

Personalized top-n influential community search over large social networks

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

User-centered analysis is one of the aims of online community search. In this paper, we study personalized top-n influential community search that has a practical application. Given an evolving social network, where every edge has a propagation probability, we propose a maximal pk-Clique community model, that uses a new cohesive criterion. The criterion requires that the propagation probability of each edge or each maximal influence path between two vertices that is considered as an edge, is greater than p. The maximal clique problem is an NP-hard problem, and the introduction of this cohesive criterion makes things worse, as it mights add new edges to existing networks. To conduct personalized top-n influential community search efficiently in such networks, we first introduce a pruning based method. We then present search space refinement and heuristic based search approaches. To diversify the search result in one pass, we also propose a diversify algorithm which is based on a novel tree-like index. The proposed algorithms achieve more than double the efficiency of the the search performance for basic solutions. The effectiveness and efficiency of our algorithms have been demonstrated using four real datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Akkoyunlu, EA: The enumeration of maximal cliques of large graphs. Siam J Comput 2(1), 1–6 (1973)

    Article  MathSciNet  Google Scholar 

  2. Bron, C, Kerbosch, J: Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16(9), 575–577 (1973)

    Article  Google Scholar 

  3. Chang, L, Yu, X, Qin, L: Fast maximal cliques enumeration in sparse graphs. Algorithmica 66(1), 173–186 (2013)

    Article  MathSciNet  Google Scholar 

  4. Chen, L, Liu, C, Zhou, R, Li, J, Yang, X, Wang, B: Maximum co-located community search in large scale social networks. Proc VLDB Endowm 11, 1233–1246 (2018)

    Article  Google Scholar 

  5. Chen, S, Fan, J, Li, G, Feng, J, Tan, Kl, Tang, J: Online topic-aware influence maximization. Proc VLDB Endow 8(6), 666–677 (2015)

    Article  Google Scholar 

  6. Chen, W, Wang, C, Wang, Y: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Acm Sigkdd International conference on knowledge discovery and data mining (2010)

  7. Cheng, J, Ke, Y, Fu, WC, Yu, JX, Zhu, L: Finding maximal cliques in massive networks. Acm Trans Datab Syst 36(4), 1–34 (2011)

    Article  Google Scholar 

  8. Cui, W, Xiao, Y, Wang, H, Lu, Y, Wang, W: Online search of overlapping communities. In: Proceedings of the ACM SIGMOD, pp 277–288 (2013)

  9. Danon, L, Duch, J, Díaz-Guilera, A, Arenas, A: Comparing community structure identification (2005)

  10. Eppstein, D, Maarten, L, Strash, D: Listing all maximal cliques in sparse graphs in near-optimal time. Comput Sci 6506, 403–414 (2010)

    MathSciNet  MATH  Google Scholar 

  11. Fang, Y, Cheng, R, Luo, S, Hu, J: Effective community search for large attributed graphs. Proc Vldb Endowm 9(12), 1233–1244 (2016)

    Article  Google Scholar 

  12. Fei, B, Chang, L, Lin, X, Zhang, W: An optimal and progressive approach to online search of top-k important communities. Proc Vldb Endowm, 11(9) (2018)

  13. Focacci, L: MILANO: cost-based domain filtering. In: International conference on principles and practice of constraint programming (1999)

    Chapter  Google Scholar 

  14. Goldenberg, J, Libai, B, Muller, E: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3), 211–223 (2001)

    Article  Google Scholar 

  15. Granovetter, MS: Threshold models of collective behavior (1978)

    Article  Google Scholar 

  16. Huang, X, Cheng, H, Qin, L, Tian, W, Yu, JX: Querying k-truss community in large and dynamic graphs. In: Proceedings of the ACM SIGMOD, pp 1311–1322 (2014)

  17. Huang, X, Lakshmanan, LVS, Xu, J: Community search over big graphs: models, algorithms, and opportunities. In: IEEE International conference on data engineering, pp 1451–1454 (2017)

  18. Huang, X, Lakshmanan, LVS, Xu, J: Community search over big graphs: models, algorithms, and opportunities. In: Proceedings of the ICDE, pp 1451–1454 (2017)

  19. Kempe, D, Kleinberg, J, Tardos, E: Maximizing the spread of influence through a social network. In: Proceedings of the ACM SIGKDD, pp 137–146 (2003)

  20. Lee, JR, Chung, CW: A query approach for influence maximization on specific users in social networks. IEEE Trans Knowl Data Eng 27(2), 340–353 (2015)

    Article  MathSciNet  Google Scholar 

  21. Li, J, Liu, C, Yu, JX, Chen, Y, Sellis, T, Culpepper, JS: Personalized influential topic search via social network summarization. In: Proceedings of the ICDE, pp 17–18 (2017)

  22. Li, J, Wang, X, Deng, K, Yang, X, Sellis, T, Yu, JX: Most influential community search over large social networks. In: Proceedings of the ICDE, pp 871–882 (2017)

  23. Li, RH, Yu, JX, Mao, R: Efficient core maintenance in large dynamic graphs. IEEE Trans Knowl Data Eng 26(10), 2453–2465 (2014)

    Article  Google Scholar 

  24. Mcauley, J, Leskovec, J: Learning to discover social circles in ego networks. In: International conference on neural information processing systems (2012)

  25. Newman, ME, Girvan, M: Finding and evaluating community structure in networks. Phys Rev E 69(2), 026113 (2004)

    Article  Google Scholar 

  26. Ruan, J, Zhang, W: An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In: Seventh IEEE international conference on data mining, pp 643–648 (2007)

  27. Satuluri, V, Parthasarathy, S: Scalable graph clustering using stochastic flows: applications to community discovery. In: Proceedings of the ACM SIGKDD, pp 737–746 (2009)

  28. Schmidt, MC, Samatova, NF, Thomas, K, Park, BH: A scalable, parallel algorithm for maximal clique enumeration. J Parallel Distrib Comput 69(4), 417–428 (2009)

    Article  Google Scholar 

  29. Shuang, Q, Jian, C, Xi, Z, Niu, B, Lu, H, Shuang, Q, Jian, C, Xi, Z, Niu, B, Lu, H: Community discovering guided cold-start recommendation: a discriminative approach. In: IEEE International conference on multimedia and expo (2014)

  30. Tomita, E, Tanaka, A, Takahashi, H: The worst-case time complexity for generating all maximal cliques and computational experiments. Theor Comput Sci 363 (1), 28–42 (2006)

    Article  MathSciNet  Google Scholar 

  31. Wang, J, Cheng, J, Fu, WC: Redundancy-aware maximal cliques. In: Proceedings of the ACM SIGKDD, pp 122–130 (2013)

  32. Wang, M, Wang, C, Yu, JX, Zhang, J: Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. Proceed Vldb Endow 8(10), 998–1009 (2015)

    Article  Google Scholar 

  33. Yuan, L, Qin, L, Lin, X, Chang, L, Zhang, W: Diversified top-k clique search. In: 2015 IEEE 31st international conference on data engineering (ICDE), vol. 00, pp 387–398 (2015)

  34. Yue, W, Meliou, A, Miklau, G: Rc-index: diversifying answers to range queries. Proc Vldb Endow 11(7), 773–786 (2018)

    Article  Google Scholar 

  35. Zhou, J, Fan, J, Wang, J, Wang, X, Li, L: Cost-efficient viral marketing in online social networks. World Wide Web (2018)

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61572165, 61803135), the Natural Science Foundation of Zhejiang Province (No. LZ15F 020003). Xiaoyi Fu’s work is supported by Hong Kong Research Grants Council (No. 12200817, 12201615 and 12258116 ).

Author information

Authors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Jian Xu, Yiming Wu, Ming Luo, Ming Xu & Ning Zheng

  2. Hong Kong Baptist University, Hongkong, China

    Xiaoyi Fu

Authors
  1. Jian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyi Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yiming Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ning Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyi Fu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Special Issue on Web Information Management and Applications

Guest Editors: Yi Cai and Jianliang Xu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, J., Fu, X., Wu, Y. et al. Personalized top-n influential community search over large social networks. World Wide Web 23, 2153–2184 (2020). https://doi.org/10.1007/s11280-020-00788-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-020-00788-w

Keywords

Search

Navigation