Abstract
We study multi-influence competing in social networks. First we propose Timeliness Independent Cascade (TIC) model - a natural multi-influence propagation model. Second we propose FairInf problem: given several companies and their budgets, how to choose their separated seeds such that the overall influence spread is maximized and each individual company’s influence spread is fairly distributed. Third, we prove that when seeds for other companies are fixed, a company’s influence spread is monotone and submodular, which means greedy algorithm has the ratio of \((1-1/e)\). We design a greedy algorithm MG which runs quickly. At last, we conduct extensive experiments on real world social networks of different scales, and evaluate that our algorithm achieves the design goal.
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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). doi:10.1007/978-3-540-77105-0_31
Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 539–550. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17572-5_48
Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: WWW 2011 (2011)
Cai, J.L.Z., Yan, M., Li, Y.: Using crowdsourced data in location-based social networks to explore influence maximization. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016
Carnes, T., Nagarajan, C., Wild, S.M., Van Zuylen, A.: Maximizing influence in a competitive social network: a follower’s perspective. In: ICEC 2007 (2007)
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 2010, pp. 379–390 (2010)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD 2010, pp. 1029–1038. ACM (2010)
Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: ICDM 2010 (2010)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD 2001, pp. 57–66. ACM (2001)
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12, 211–223 (2001)
Han, M., Yan, M., Cai, Z., Li, Y.: An exploration of broader influence maximization in timeliness networks with opportunistic selection. J. Netw. Comput. Appl. 63, 39–49 (2016)
Han, M., Yan, M., Cai, Z., Li, Y., Cai, X., Yu, J.: Influence maximization by probing partial communities in dynamic online social networks. Trans. Emerg. Telecommun. Technol. 28, e3054 (2016)
He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SDM 2012 (2012)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD 2003, pp. 137–146. ACM (2003)
Kostka, J., Oswald, Y.A., Wattenhofer, R.: Word of mouth: rumor dissemination in social networks. In: Shvartsman, A.A., Felber, P. (eds.) SIROCCO 2008. LNCS, vol. 5058, pp. 185–196. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69355-0_16
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: KDD 2007 (2007)
Li, S., Zhu, Y., Li, D., Kim, D., Huang, H.: Rumor restriction in online social networks. In: IPCCC 2013 (2013)
Lu, W., Bonchi, F., Goya, A., Laksmanan, L.V.S.: The bang for the buck: fair competitive viral marketing from the host perspective. In: KDD 2013 (2013)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM 2011, pp. 287–296 (2011)
Pathak, N., Banerjee, A., Srivastava, J.: A generalized linear threshold model for multiple cascades. In: ICDM 2010 (2010)
Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39718-2_23
Shi, T., Cheng, S., Cai, Z., Li, Y., Li, J.: Retrieving the maximal time-bounded positive influence set from social networks. Pers. Ubiquit. Comput. 20(5), 717–730 (2016)
Trpevski, D., Tang, W.K.S., Kocarev, L.: Model for rumor spreading over networks. Phys. Rev. E 81, 056102 (2010)
Zhu, Y., Li, D., Guo, H., Pamula, R.: New competitive influence propagation models in social networks. In: 2014 10th International Conference on Mobile Ad-hoc and Sensor Networks, pp. 257–262, December 2014
Zhu, Y., Li, D., Zhang, Z.: Minimum cost seed set for competitive social influence. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9, April 2016
Zhu, Y., Wu, W., Bi, Y., Wu, L., Jiang, Y., Xu, W.: Better approximation algorithms for influence maximization in online social networks. J. Comb. Optim. 30, 97–108 (2015)
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This work is partly supported by National Natural Science Foundation of China under grant 11671400.
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Yu, Y., Jia, J., Li, D., Zhu, Y. (2017). Fair Multi-influence Maximization in Competitive Social Networks. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_23
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DOI: https://doi.org/10.1007/978-3-319-60033-8_23
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