Skip to main content
SpringerLink
Log in

CoIM: Community-Based Influence Maximization in Social Networks

  • Conference paper
  • First Online:
Advanced Informatics for Computing Research (ICAICR 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 956))

Abstract

Influence maximization (IM) is the problem of identifying k most influential users (seed) in social networks to maximize influence spread. Despite some recent development achieved by the state-of-the-art greedy IM techniques, these works are not time-efficient for large-scale networks. To solve time-efficiency issue, we propose Community-based Influence Maximization (CoIM) algorithm. CoIM first partitions the network into sub-networks. Then it selects influential users from sub-networks based on their local influence. The experimental results on both synthetic and real datasets show that proposed algorithm performs better than greedy regarding time with the almost same level of memory-consumption and influence spread.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www-personal.umich.edu/~mejn/netdata/dolphins.zip.

References

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

    Article  Google Scholar 

  2. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 199–208. ACM, New York (2009)

    Google Scholar 

  3. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 57–66. ACM, New York (2001)

    Google Scholar 

  4. Feng, Z., Xu, X., Yuruk, N., Schweiger, T.A.J.: A novel similarity-based modularity function for graph partitioning. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 385–396. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74553-2_36

    Chapter  Google Scholar 

  5. Gomez Rodriguez, M., Schölkopf, B.: Influence maximization in continuous time diffusion networks. In: Proceedings of the 29th International Conference on Machine Learning, pp. 313–320. Omnipress, New York, July 2012

    Google Scholar 

  6. Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B.: Influence estimation and maximization in continuous-time diffusion networks. ACM Trans. Inf. Syst. 34(2), 9:1–9:33 (2016)

    Article  Google Scholar 

  7. Goyal, A., Lu, W., Lakshmanan, L.V.: Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 47–48. ACM, New York (2011)

    Google Scholar 

  8. Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.L.: Influence maximization in trajectory databases. IEEE Trans. Knowl. Data Eng. 29(3), 627–641 (2017)

    Article  Google Scholar 

  9. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146. ACM, New York (2003)

    Google Scholar 

  10. Kundu, S., Murthy, C.A., Pal, S.K.: A new centrality measure for influence maximization in social networks. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 242–247. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21786-9_40

    Chapter  Google Scholar 

  11. Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web 1(1) (2007)

    Article  Google Scholar 

  12. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 420–429. ACM, New York (2007)

    Google Scholar 

  13. Li, G., Chen, S., Feng, J., Tan, K., Li, W.: Efficient location-aware influence maximization. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pp. 87–98. ACM, New York (2014)

    Google Scholar 

  14. Li, H., Bhowmick, S.S., Sun, A., Cui, J.: Conformity-aware influence maximization in online social networks. VLDB J. 24(1), 117–141 (2015)

    Article  Google Scholar 

  15. Li, Y., Zhang, D., Tan, K.L.: Real-time targeted influence maximization for online advertisements. Proc. VLDB Endow. 8(10), 1070–1081 (2015)

    Article  Google Scholar 

  16. Lin, S.C., Lin, S.D., Chen, M.S.: A learning-based framework to handle multi-round multi-party influence maximization on social networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 695–704. ACM, New York (2015)

    Google Scholar 

  17. Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)

    Article  Google Scholar 

  18. Mahajan, V., Muller, E., Bass, F.M.: New product diffusion models in marketing: a review and directions for research. J. Mark. 54(1), 1–26 (1990)

    Article  Google Scholar 

  19. Paul, J.: The distribution of the flora in the alpine zone.1. New Phytol. 11(2), 37–50 (1912)

    Article  Google Scholar 

  20. Sviridenko, M.: A note on maximizing a submodular set function subject to a knapsack constraint. Oper. Res. Lett. 32(1), 41–43 (2004)

    Article  MathSciNet  Google Scholar 

  21. Teng, Y.W., Tai, C.H., Yu, P.S., Chen, M.S.: Revenue maximization on the multi-grade product, pp. 576–584 (2018)

    Chapter  Google Scholar 

  22. Wang, Y., Feng, X.: A potential-based node selection strategy for influence maximization in a social network. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds.) ADMA 2009. LNCS (LNAI), vol. 5678, pp. 350–361. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03348-3_34

    Chapter  Google Scholar 

  23. Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: KDD (2010)

    Google Scholar 

  24. Wu, P., Pan, L.: Scalable influence blocking maximization in social networks under competitive independent cascade models. Comput. Netw. 123, 38–50 (2017)

    Article  Google Scholar 

  25. Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 671–680. ACM, New York (2012)

    Google Scholar 

  26. Zhu, Y., Li, D., Zhang, Z.: Minimum cost seed set for competitive social influence. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221 005, India

    Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar & Bhaskar Biswas

Authors
  1. Shashank Sheshar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kuldeep Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ajay Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bhaskar Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashank Sheshar Singh .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, Haryana, India

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  3. National Chung Cheng University, Minxiong Township, Chiayi County, Taiwan

    Pao-Ann Hsiung

  4. Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  5. Saint Mary’s University, Halifax, NS, Canada

    Pawan Lingras

  6. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, S.S., Singh, K., Kumar, A., Biswas, B. (2019). CoIM: Community-Based Influence Maximization in Social Networks. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 956. Springer, Singapore. https://doi.org/10.1007/978-981-13-3143-5_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3143-5_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3142-8

  • Online ISBN: 978-981-13-3143-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation