ABSTRACT
Since the tremendous success of social networking websites, the related analytical research has been widely studied. Among these studies, social influence has been a significant and popular topic. We rely on the social influence model to predict and learn the influence diffusion process. However, traditional models only categorize nodes into two types of states, active and inactive. In addition, most previous models have only taken positive influences into account. Moreover, if inactive nodes are influenced successfully and turn into active nodes, these nodes cannot change their states forever. In this work, we not only break the above limitations but also propose a novel propagation method in our model. We proposes five states to represent the multiple states of influence. According to the new propagation method, the strength of the social influence may be reduced over time. Eventually, we utilize the measurement of precisions to compare with related models. The proposed multi-state model outperforms other two-state models in precisions of prediction. The experimental results show the superiority of multiple states.
- E. Bakshy, D. Eckles, R. Yan, and I. Rosenn. Social influence in social advertising: evidence from field experiments. In ACM Conference on Electronic Commerce, pages 146--161, 2012. Google ScholarDigital Library
- R. M. Bond, C. J. Fariss, J. J. Jones, A. D. I. Kramer, C. Marlow, J. E. Settle, and J. H. Fowler. A 61-million-person experiment in social influence and political mobilization. Nature, 489(11421):295-- 298, 2012.Google ScholarCross Ref
- W. Chen, A. Collins, R. Cummings, T. Ke, Z. Liu, D. Rincon, X. Sun, Y. Wang, W. Wei, and Y. Yuan. Influence maximization in social networks when negative opinions may emerge and propagate. In SIAM International Conference on Data Mining, pages 379--390, 2011.Google ScholarCross Ref
- N. A. Christakis and J. H. Fowler. Connected: The surprising power of our social networks and how they shape our lives. In Back Bay Books, 2011.Google Scholar
- D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 160--168, 2008. Google ScholarDigital Library
- P. Domingos and M. Richardson. Mining the network value of customers. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 57--66, 2001. Google ScholarDigital Library
- J.-L. Duan, S. Prasad, and J.-W. Huang. Discovering unknown but interesting items on personal social network. In Proceedings of the Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining, pages 145--156, 2012. Google ScholarDigital Library
- J. Goldenberg, B. Libai, and E. Muller. Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata. In Academy of Marketing Science Review, pages 1--18, 2001.Google Scholar
- M. Granovetter. Threshold models of collective behavior. In The American Journal of Sociology, pages 1420--1443, 1978.Google ScholarCross Ref
- D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 137--146, 2003. Google ScholarDigital Library
- H. Ma, H. Yang, M. R. Lyu, and I. King. Mining social networks using heat diffusion processes for marketing candidates selection. In ACM Conference on Information and Knowledge Management, pages 233--242, 2008. Google ScholarDigital Library
- M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 61--70, 2002. Google ScholarDigital Library
- L. Schiffman, L. Kanuk, and J. Wisenblit. Consumer behavior 10th edition. In Prentice Hall, pages 260--268, 370--374, 2009.Google Scholar
Multi-state Open Opinion Model based on Positive and Negative Social Influences
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