Abstract
Recently, the phenomenon of influence propagation becomes a hot topic in social networks. However, few existing influence models study the influence from communities, which has a large range of applications. In this paper, we use the charged system model to represent the social influence. This model provides a natural description about the behaviors of influence and explains why the influence makes communities expand. Based on this physical model, we propose two objective functions for choosing proper candidates to enlarge a community, considering of the cost and benefit issue. Then a linear programming approach is given to maximize those two objective functions. We validate our ideas and algorithm using two real-world networks. The results demonstrate that our model can choose excellent propagation candidates for a specific community, comparing to other two algorithms.
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References
Aggarwal, C.C.: Social Network Data Analytics. Springer (2011)
Aggarwal, C.C., Yu, P.S.: Online analysis of community evolution in data streams. In: SDM (2005)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)
Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale soical networks. In: Data Mining and Knowledge Discovery (2012)
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)
Sun, T., Chen, W., Liu, Z., Wang, Y., Sun, X., Zhang, M., Lin, C.: Participation maximization based on social influence in online discussion forums. In: ICWSM (2011)
Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: KDD (2008)
Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM (2011)
Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: KDD (2006)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: WSDM (2010)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD (2001)
Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web (2007)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)
Shimp, T.A.: Advertising promotion: Supplemental aspects of integrated marketing communications. South-Western College Pub. (2002)
Hartline, J., Mirrokni, V., Sundararajan, M.: Optimal marketing strategies over social networks. In: WWW (2008)
Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics, 8th edn. Wiley (2007)
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)
Hu, Y., Chen, H., Zhang, P., Di, Z., Li, M., Fan, Y.: Comparative definition of community and corresponding identifying algorithm. Phys. Rev. (2008)
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Bi, Y., Wu, W., Wang, A., Fan, L. (2013). Community Expansion Model Based on Charged System Theory. In: Du, DZ., Zhang, G. (eds) Computing and Combinatorics. COCOON 2013. Lecture Notes in Computer Science, vol 7936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38768-5_71
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DOI: https://doi.org/10.1007/978-3-642-38768-5_71
Publisher Name: Springer, Berlin, Heidelberg
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