Abstract
We present two new seeding strategies for the Influence Maximization Problem for Viral Marketing, based on graph connectivity and spectral graph theory. Specifically, the first approach CVSP uses the cut vertices and the separation pairs as the starting seeds. The second approach ER uses the vertex ranking based on the effective resistance values of the incident edges. CVSP and ER are efficient, and can be implemented in linear and near linear time, respectively.
Experiments using the Independent Cascade diffusion model with real-world data sets show that our new seeding strategies perform significantly better than the existing methods, such as centrality measures, k-core and the state-of-the-art IMM, in particular for the scale-free networks with globally sparse, locally dense clusters with small diameters, in the final influence spread. Moreover, visual analysis enables more refined comparison between the methods, demonstrating that our methods have more globally wide influence spread pattern than other methods with locally dense influence spread pattern.
This work is supported by ARC grant DP190103301.
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References
Batagelj, V., Mrvar, A., Zaveršnik, M.: Partitioning approach to visualization of large graphs. In: GD 1999, pp. 90–97 (1999)
Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: SODA 2014, pp. 946–957 (2014)
Chen, D., Lü, L., Shang, M.S., Zhang, Y.C., Zhou, T.: Identifying influential nodes in complex networks. Phys. A 391(4), 1777–1787 (2012)
Domingos, P., Richardson, M.: Mining the network value of customers. In: SIGKDD 2001, pp. 57–66 (2001)
Gui-sheng, Y., Ji-jie, W., Hong-bin, D., Jia, L.: Intelligent viral marketing algorithm over online social network. In: ICNDC 2011, pp. 319–323 (2011)
Hopcroft, J., Tarjan, R.: Algorithm 447: efficient algorithms for graph manipulation. Commun. ACM 16(6), 372–378 (1973)
Hopcroft, J.E., Tarjan, R.E.: Dividing a graph into triconnected components. SIAM J. Comput. 2(3), 135–158 (1973)
Iyer, S., Killingback, T., Sundaram, B., Wang, Z.: Attack robustness and centrality of complex networks. PLoS ONE 8(4), e59,613 (2013)
Jacob, R., Koschützki, D., Lehmann, K.A., Peeters, L., Tenfelde-Podehl, D.: Algorithms for centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 62–82. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31955-9_4
Kaplan, A.M., Haenlein, M.: Two hearts in three-quarter time: how to waltz the social media/viral marketing dance. Bus. Horiz. 54(3), 253–263 (2011)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD 2003, pp. 137–146 (2003)
Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: ICALP 2005, pp. 1127–1138 (2005)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. Theory Comput. 11(4), 105–147 (2015)
Kitsak, M., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (TWEB) 1(1), 5–es (2007)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: SIGKDD 2007, KDD 2007, pp. 420–429 (2007)
Liu, J.G., Lin, J.H., Guo, Q., Zhou, T.: Locating influential nodes via dynamics-sensitive centrality. Sci. Rep. 6(1), 1–8 (2016)
Liu, Y., Tang, M., Zhou, T., Do, Y.: Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition. Sci. Rep. 5(1), 1–8 (2015)
Long, C., Wong, R.C.W.: Viral marketing for dedicated customers. Inf. Syst. 46, 1–23 (2014)
Lü, L., Chen, D., Ren, X.L., Zhang, Q.M., Zhang, Y.C., Zhou, T.: Vital nodes identification in complex networks. Phys. Rep. 650, 1–63 (2016)
Marner, M.R., Smith, R.T., Thomas, B.H., Klein, K., Eades, P., Hong, S.H.: Gion: interactively untangling large graphs on wall-sized displays. In: GD 2014, pp. 113–124 (2014)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: SIGKDD 2002, pp. 61–70 (2002)
Shakarian, P., Bhatnagar, A., Aleali, A., Shaabani, E., Guo, R.: Diffusion in Social Networks. SCS, Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23105-1
Spielman, D.A., Srivastava, N.: Graph sparsification by effective resistances. SIAM J. Comput. 40(6), 1913–1926 (2011)
Spielman, D.A., Teng, S.H.: Spectral sparsification of graphs. SIAM J. Comput. 40(4), 981–1025 (2011)
Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: SIGMOD 2015, pp. 1539–1554 (2015)
Wasserman, S., Faust, K., et al.: Social network analysis: methods and applications (1994)
Zhu, T., Wang, B., Wu, B., Zhu, C.: Maximizing the spread of influence ranking in social networks. Inf. Sci. 278, 535–544 (2014)
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Hong, SH., Ataides, J.P.B., Kok, R., Meidiana, A., Park, K. (2024). New Seeding Strategies for the Influence Maximization Problem. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_23
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