Abstract
Influence Maximization is the problem of choosing a small set of seed users within a larger social network in order to maximize the spread of influence under certain diffusion models. The problem has been widely studied and several solutions have been proposed. Previous work has concentrated on positive relationships between users, with little attention given to the effect of negative relationships of users and the corresponding spread of negative opinion. In this paper we study influence maximization in signed social networks and propose a new diffusion model called LT-S, which is an extension to the classical linear threshold model incorporating both positive and negative opinions. To the best of our knowledge, we are the first to study the influence maximization problem in signed social networks with opinion formation. We prove that the influence spread function under the LT-S model is neither monotone nor submodular and propose an improved R-Greedy algorithm called RLP. Extensive experiments conducted on real signed social network datasets demonstrate that our algorithm outperforms the baseline algorithms in terms of efficiency and effectiveness.
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References
Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 306–311. Springer, Heidelberg (2007)
Buchbinder, N., Feldman, M., Naor, J., Schwartz, R.: Submodular maximization with cardinality constraints. In: SODA, pp. 1433–1452 (2014)
Cartwright, D., Harary, F.: Structural balance: a generalization of Heider’s theory. Psychol. Rev. 63(5), 277–293 (1956)
Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincon, D., Sun, X., Wang, Y., Wei, W., Yuan, Y.: Influence maximization in social networks when negative opinions may emerge and propagate. In: SDM, pp. 379–390 (2011)
Chen, W., Lu, W., Zhang, N.: Time-critical influence maximization in social networks with time-delayed diffusion process. In: AAAI (2012)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)
Cheng, S., Shen, H., Huang, J., Zhang, G., Cheng, X.: Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: CIKM, pp. 509–518 (2013)
Chiang, K.Y., Hsieh, C.J., Natarajan, N., Dhillon, I.S., Tewari, A.: Prediction and clustering in signed networks: a local to global perspective. J. Mach. Learn. Res. 15(1), 1177–1213 (2014)
Clifford, P., Sudbury, A.: A model for spatial conflict. Biometrika 60(3), 581–588 (1973)
DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974)
DeMarzo, P.M., Vayanos, D., Zwiebel, J.: Persuasion bias, social influence, and unidimensional opinions. Q. J. Econ. 118(3), 909–968 (2003)
Fan, P., Wang, H., Li, P., Li, W., Jiang, Z.: Analysis of opinion spreading in homogeneous networks with signed relationships. J. Stat. Mech. Theory Exper. 2012(08), P08003 (2012)
Feige, U., Mirrokni, V.S., Vondrak, J.: Maximizing non-monotone submodular functions. SIAM J. Comput. 40(4), 1133–1153 (2011)
Feng, S., Chen, X., Cong, G., Zeng, Y., Chee, Y.M., Xiang, Y.: Influence maximization with novelty decay in social networks. In: AAAI, pp. 37–43 (2014)
Friedkin, N.E., Johnsen, E.C.: Social influence and opinions. J. Math. Sociol. 15(3–4), 193–206 (1990)
Heider, F.: Attitudes and cognitive organization. J. Psychol. 21(1), 107–112 (1946)
Jung, K., Heo, W., Chen, W.: Irie: scalable and robust influence maximization in social networks. In: ICDM, pp. 918–923 (2012)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)
Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: mining a social network with negative edges. In: WWW, pp. 741–750 (2009)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: WWW, pp. 641–650 (2010)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: KDD, pp. 420–429 (2007)
Li, Y., Chen, W., Wang, Y., Zhang, Z.L.: Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In: WSDM, pp. 657–666. ACM (2013)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: ICDM, pp. 439–448 (2012)
Rodriguez, M.G., Schölkopf, B.: Influence maximization in continuous time diffusion networks. In: ICML, pp. 313–320 (2012)
Yang, B., Cheung, W.K., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)
Zhang, H., Dinh, T.N., Thai, M.T.: Maximizing the spread of positive influence in online social networks. In: ICDCS, pp. 317–326. IEEE (2013)
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Shen, C., Nishide, R., Piumarta, I., Takada, H., Liang, W. (2015). Influence Maximization in Signed Social Networks. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_27
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DOI: https://doi.org/10.1007/978-3-319-26190-4_27
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