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Conformity-aware influence maximization in online social networks

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Abstract

Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by state-of-the-art greedy im techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large real-world networks. Secondly, although extensive research in social psychology suggests that humans will readily conform to the wishes or beliefs of others, surprisingly, existing im techniques are conformity-unaware. That is, they only utilize an individual’s ability to influence another but ignores conformity (a person’s inclination to be influenced) of the individuals. In this paper, we propose a novel conformity-aware cascade (\({\textsc {c}}^2\)) model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying data for estimating influence spreads. We also propose a variant of this model called \(\textsc {c}^3\) model that supports context-specific influence and conformity of nodes. A salient feature of these models is that they are aligned to the popular social forces principle in social psychology. Based on these models, we propose a novel greedy algorithm called cinema that generates high-quality seed set for the im problem. It first partitions, the network into a set of non-overlapping subnetworks and for each of these subnetworks it computes the influence and conformity indices of nodes by analyzing the sentiments expressed by individuals. Each subnetwork is then associated with a cog-sublist which stores the marginal gains of the nodes in the subnetwork in descending order. The node with maximum marginal gain in each cog-sublist is stored in a data structure called mag-list. These structures are manipulated by cinema to efficiently find the seed set. A key feature of such partitioning-based strategy is that each node’s influence computation and updates can be limited to the subnetwork it resides instead of the entire network. This paves way for seamless adoption of cinema on a distributed platform. Our empirical study with real-world social networks comprising of millions of nodes demonstrates that cinema as well as its context-aware and distributed variants generate superior quality seed set compared to state-of-the-art im approaches.

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Notes

  1. We have experimented with different initial values. All strategies converge to the same results with different number of iterations (see Appendix D of ESM for details).

  2. glaros.dtc.umn.edu/gkhome/cluto/cluto/overview

  3. Net and Phy are downloaded from http://research.microsoft.com/enus/people/weic/graphdata.zip.

  4. http://snap.stanford.edu/data/wiki-Talk.html.

  5. http://snap.stanford.edu/data/soc-livejournal1.html.

  6. http://dev.twitter.com/doc.

  7. Given a user-specified keyword (trend), this module returns up to 15 result pages, each containing 100 tweets.

  8. We thank the first author for sending us the code.

  9. Randomly selected from a twitter corpus which random-sampled 21,410,330 tweets during Jan 22 to 29, 2013.

References

  1. A-lias: Lingpipe 4.0.1. http://alias-i.com/lingpipe

  2. Aronson, E., Wilson, T.D., Akert, R.M.: Social Psychology, 5th edn. Prentice Hall, Englewood Cliffs NJ (2004)

    Google Scholar 

  3. Asch, S.: Opinions and social pressure. Sci. Am. 193, 31–35 (1955)

  4. Asch, S.E.: Effects of group pressure upon the modification and distortion of judgment In: Guetzkow, H. (ed.) Groups, leadership and men, pp. 177–190. Carnegie Press, Pittsburgh, PA (1951)

  5. Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. In: IEEE ICDM (2012)

  6. Chen, W., Collins, A., et al.: Influence maximization in social networks when negative opinions may emerge and propagate. In: SDM (2011)

  7. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: ACM KDD (2010)

  8. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: ACM KDD (2009)

  9. Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: IEEE ICDM (2010)

  10. Cialdini, R.B., Goldstein, N.J.: Social influence: Compliance and conformity. Annu. Rev. Psychol. 55, 591–621 (2004)

  11. Crano, W.D.: Effects of sex, response order, and expertise in conformity: a dispositional approach. Sociometry 33, 239–252 (1970)

  12. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: OSDI (2004)

  13. Edelson, M., Sharot, T., Dolan, R.J., Dudai, Y.: Following the crowd: brain substrates of long-term memory conformity. Science 333(6038), 108–111 (2011)

  14. Epley, N., Gilovich, T.: Just going along: nonconscious priming and conformity to social pressure. J. Exp. Soc. Psychol. 35(6), 578–589 (1999)

  15. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)

  16. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: ACM WSDM (2010)

  17. Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. In: PVLDB 5(1) (2011)

  18. Goyal, A., Lu, W., Lakshmanan, L.V.S.: Simpath: An efficient algorithm for influence maximization under the linear threshold model. In: IEEE ICDM (2011)

  19. James, K., Zollman, S.: Social Structure and the Effects of Conformity. Springer, Berlin (2008)

  20. Jiang, Q., Song, G., et al.: Simulated annealing based influence maximization in social networks. In: AAAI (2011)

  21. Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: ACM WSDM (2011)

  22. Jung, K., Heo, W., Chen, W.: IRIE: Scalable and robust influence maximization in social networks. In: IEEE ICDM (2012)

  23. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: ACM KDD (2003)

  24. Kim, J., Kim, S.K., Yu, H.: Scalable and parallelizable processing of influence maximization for large-scale social networks. In: IEEE ICDE (2013)

  25. Klucharev, V., Munneke, M.A.M., et al.: Downregulation of the posterior medial frontal cortex prevents social conformity. J. Neurosci. 31(33), 11934–11940 (2011)

  26. Latane, B.: The psychology of social impact. Am. Psychol. 36(4), 343–356 (1981)

    Article  Google Scholar 

  27. Leskovec, J., Huttenlocher, D.P., Kleinberg, J.M.: Predicting positive and negative links in online social networks. In: ACM WWW (2010)

  28. Leskovec, J., Krause, A., et al.: Cost-effective outbreak detection in networks. In: ACM KDD (2007)

  29. Li, H., Bhowmick, S.S., Sun, A.: Casino: towards conformity-aware social influence analysis in online social networks. In: ACM CIKM (2011)

  30. Li, H., Bhowmick, S.S., Sun, A.: Cinema: conformity-aware greedy algorithm for influence maximization in online social networks. In: ACM EDBT (2013)

  31. Li, F., Ooi, B.C., Ozsu, T., Wu, S.: Distributed data management using MapReduce. ACM Comp. Surv. 46(3), 31:1–31:42 (2014)

  32. Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: ACM CIKM (2009)

  33. Liu, L., Tang, J., et al.: Mining Topic-level Influence in Heterogeneous Networks. In: CIKM (2010)

  34. Pedro, D., Matt, R.: Mining the network value of customers. In: ACM KDD (2001)

  35. Pellegrini, F., Roman, J.: Scotch: A software package for static mapping by dual recursive bipartitioning of process and architecture graphs. In: HPCN. Springer, Berlin (1996)

  36. Pornpitakpan, C.: The persuasiveness of source credibility: a critical review of five decades’ evidence. J. Appl. Soc. Psychol. 34(2), 243–281 (2004)

    Article  Google Scholar 

  37. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: ACM KDD (2002)

  38. Schloegel, K., Karypis, G., Kumar, V.: Parallel static and dynamic multi-constraint graph partitioning. Concurr. Comput. Pract. Exp. 14(3), 219–240 (2002)

  39. Stricklanda, B.R., Crowne, D.P.: Conformity under conditions of simulated group pressure as a function of the need for social approval. J. Soc. Psychol. 58(1), 171–181 (1962)

  40. Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: ACM KDD (2009)

  41. Tang, J., Wu, S., Sun, J.: Confluence: Conformity influence in large social networks. In: ACM KDD (2013)

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

  43. Yu, X., Liu, Y., et al.: A quality-aware model for sales prediction using reviews. In: ACM WWW (2010)

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Acknowledgments

Hui Li and Jiangtao Cui are supported by National Nature Science Foundation of China (under NSFC Grant Nos. 61202179 and 61173089), SRF for ROCS, SEM and the Fundamental Research Funds.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, China

    Hui Li & Jiangtao Cui

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

    Sourav S. Bhowmick & Aixin Sun

Authors
  1. Hui Li
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  2. Sourav S. Bhowmick
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  3. Aixin Sun
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  4. Jiangtao Cui
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Corresponding author

Correspondence to Sourav S. Bhowmick.

Additional information

Part of H. Li’s work was done when he was pursuing doctoral degree in Nanyang Technological University.

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Li, H., Bhowmick, S.S., Sun, A. et al. Conformity-aware influence maximization in online social networks. The VLDB Journal 24, 117–141 (2015). https://doi.org/10.1007/s00778-014-0366-x

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  • DOI: https://doi.org/10.1007/s00778-014-0366-x

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