Skip to main content
SpringerLink
Log in

Community-based influence maximization for viral marketing

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Derived from the idea of word-to-mouth advertising and with applying information diffusion theory, viral marketing attracts wide research interests because of its business value. As an effective marketing strategy, viral marketing is to select a small set of initial users based on trust among close social circles of friends or families so as to maximize the spread of influence in the social network. In this paper, we propose a new community-based influence maximization method for viral marketing that integrates community detection into influence diffusion modeling, instead of performing community detection independently, to improve the performance. We first build a comprehensive latent variable model which captures community-level topic interest, item-topic relevance and community membership distribution of each user, and we propose a collapsed Gibbs sampling algorithm to train the model. Then we infer community-to-community influence strength using topic-irrelevant influence and community topic interest, and further infer user-to-user influence strength using community-to-community influence strength and community membership distribution of each user. Finally we propose a community-based heuristic algorithm to mine influential nodes that selects the influential nodes with a divide-and-conquer strategy, considering both topic-aware and community-relevant to enhance quality and improve efficiency. Extensive experiments are conducted to evaluate effectiveness and efficiency of our proposals. The results validate our ideas and show the superiority of our method compared with state-of-the-art influence maximization algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://www.yelp.com/

  2. https://www.digg.com/

References

  1. Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of SIGKDD, pp 57–66

  2. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of SIGKDD, pp 61–70

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

  4. Galhotra S, Arora A, Roy S (2016) Holistic influence maxi- mization: combining scalability and efficiency with opinion-aware models. In: Proceedings of SIGMOD, pp 743–758

  5. Goyal A, Lu W, Lakshmanan LV (2011) CELF++: opti- mizing the greedy algorithm for influence maximization in social networks. In: Proceedings of WWW, pp 47–48

  6. Kundu S, Murthy C, Pal SK (2011) A new centrality measure for influence maximization in social networks. In: Proceedings of international conference on pattern recognition and machine intelligence, pp 242–247

  7. Leskovec J, Krause A, Guestrin C, Faloutsos C , Van Briesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: Proceedings of SIGKDD, pp 420–429

  8. Cheng S, Shen H, Huang J (2012) StaticGreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of ACM international conference on information and knowledge management, pp 509–518

  9. Tang Y, Shi Y, Xiao X (2015) Influence maximization in near-linear time: a martingale approach. In: Proceedings of SIGMOD, pp 1593–1554

  10. Tang Y, Xiao X, Shi Y (2014) Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of SIGMOD, pp 75–86

  11. Chen W, Wang Y, Yang S (2009) Efficient influence maxi- mization in social networks. In: Proceedings of SIGKDD, pp 199–208

  12. Wang Y, Feng X (2009) A potential-based node selection strategy for influence maximization in a social network. In: Proceedings of international conference on advanced data mining and applications, pp 350–361

  13. Su J, Havens TC (2015) Quadratic program-based modularity maximization for fuzzy community detection in social networks. IEEE Trans Fuzzy Syst 23(5):1356–1371

    Article  Google Scholar 

  14. Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes. In: Proceedings of ICDM, pp 1151–1156

  15. Li J, Hu X, Jian L, Liu H (2017) Toward time-evolving feature selection on dynamic networks. In: Proceedings of ICDM, pp 1003–1008

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

    Article  Google Scholar 

  17. Kalish S (1985) A new product adoption model with price, advertising, and uncertainty. Manag Sci 31(12):1569–1585

    Article  MATH  Google Scholar 

  18. Luo ZL, Cai WD, Li YJ (2012) Recent progress in data engineering and internet technology. Springer, Berlin

    Google Scholar 

  19. Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of SIGKDD, pp 1029–1038

  20. Cao T, Wu X, Wang S, Hu X (2010) Oasnet: an optimal allocation approach to influence maximization in modular social networks. In: Proceedings of ACM symposium on applied computing, pp 1088–1094

  21. Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):1–6

    Article  Google Scholar 

  22. Wang Y, Cong G, Song G, Xie K (2010) Community-based greedy algorithm for mining top-K influential nodes in mobile social networks. In: Proceedings of SIGKDD, pp 1039–1048

  23. Chen YC, Zhu WY, Lee WC, Lee S-Y (2014) Cim: community-based influence maximization in social networks. ACM Trans Intell Syst Technol 5(2):1–25

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res Arch 3:993–1022

    MATH  Google Scholar 

  26. Yan X, Guo J, Lan Y, Cheng X (2013) A biterm topic model for short texts. In: Proceedings of international conference on World Wide Web, pp 1445–1456

  27. Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. In: Proceedings of SIGKDD, pp 233–242

  28. Yang Z, Kotov A, Mohan A, Lu S (2015) Parametric and non- parametric user-aware sentiment topic models. In: Proceedings of SIGIR, pp 413–422

  29. Iwata T, Yamada T, Sakurai Y, Ueda N (2010) Online multiscale dynamic topic models. In: Proceedings of SIGKDD, pp 663–672

  30. Liang S, Yilmaz E, Kanoulas E (2016) Dynamic clustering of streaming short documents. In: Proceedings of SIGKDD, pp 995–1004

  31. Zhao Y, Liang S, Ren Z, Ma J, Yilmaz E, Rijke M D (2016) Explainable user clustering in short text streams. In: Proceedings of SIGIR, pp 155–164

  32. Cha Y, Cho J (2012) Social-network analysis using topic models. In: Proceedings of SIGIR, pp 565–574

  33. Guo W, Wu S, Wang L, Tan T (2015) Social-relational topic model for social networks. In: Proceedings of CIKM, pp 1731–1734

  34. Bi B, Tian Y, Balmin A, Balmin A, Cho J (2014) Scalable topic-specific influence analysis on microblogs. In: Proceedings of WSDM, pp 513–522

  35. Barbieri N, Bonchi F, Manco G (2012) Topic-aware social influence propagation models. In: Proceedings of ICDM, pp 81–90

  36. Chen W, Wei L, Zhang N (2012) Time-critical influence maximization in social networks with time-delayed diffusion process. In: Proceedings of AAAI, pp 592–598

  37. Hu Z, Yao J, Cui B, Xing E (2015) Community level diffusion extraction. In: Proceedings of SIGMOD, pp 1555–1569

  38. Zhang Y, Lyu T, Zhang Y (2017) Hierarchical community-level information diffusion modeling in social networks. In: Proceedings of SIGIR, pp 753–762

  39. Liu L, Tang J, Han J, Jiang M, Yang S (2010) Mining topic-level influecne in heterogeneous networks. In: Proceedings of CIKM, Toronto, pp 199–208

  40. Heinrich G (2008) Parameter estimation for text analysis. Technical Report, pp 1–32

  41. Zhang Y, Chen H, Lu J, Zhang G (2017) Detecting and predicting the topic change of knowledge-based systems: a topic-based bibliometric analysis from 1991 to 2016. Knowl.Based Syst 133:255–268

    Article  Google Scholar 

  42. Della Rocca P, Senatore S, Loia V (2017) A semantic-grained perspective of latent knowledge modeling. Inf Fusion 36:52–67

    Article  Google Scholar 

  43. Rizun N, Ossowska K, Taranenko Y (2018) Modeling the customer’s contextual expectations based on latent semantic analysis algorithms. In: Światek J, Borzemski L, Wilimowska Z (eds) Information systems architecture and technology: proceedings of 38th international conference on information systems architecture and technology - ISAT 2017. ISAT 2017. Advances in intelligent systems and computing, vol 656. Springer, Cham

  44. Wang Z, Gu S, Xu X (2018) GSLDA: LDA-based group spamming detection in product reviews. Appl Intell 48:3094

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Key R & D Program of China Project #2017YFB0203201 and Australian Research Council Discovery Project DP150104871.

Author information

Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Huimin Huang, Hong Shen, Zaiqiao Meng, Huajian Chang & Huaiwen He

  2. School of Computer Science, University of Adelaide, Adelaide, Australia

    Hong Shen

Authors
  1. Huimin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zaiqiao Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huajian Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huaiwen He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Shen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, H., Shen, H., Meng, Z. et al. Community-based influence maximization for viral marketing. Appl Intell 49, 2137–2150 (2019). https://doi.org/10.1007/s10489-018-1387-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1387-8

Keywords

Search

Navigation