Abstract
We address the problem of discovering a different kind of influential nodes, which we call ”super-mediator”, i.e. those nodes which play an important role to pass the information to other nodes, and propose a method for discovering super-mediators from information diffusion samples without using a network structure. We divide the diffusion sequences in two groups (lower and upper), each assuming some probability distribution, find the best split by maximizing the likelihood, and rank the nodes in the upper sequences by the F-measure. We apply this measure to the information diffusion samples generated by two real networks, identify and rank the super-mediator nodes. We show that the high ranked super-mediators are also the high ranked influential nodes when the diffusion probability is large, i.e. the influential nodes also play a role of super-mediator for the other source nodes, and interestingly enough that when the high ranked super-mediators are different from the top ranked influential nodes, which is the case when the diffusion probability is small, those super-mediators become the high ranked influential nodes when the diffusion probability becomes larger. This finding will be useful to predict the influential nodes for the unexperienced spread of new information, e.g. spread of new acute contagion.
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References
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 211–223 (2001)
Newman, M.E.J., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Physical Review E 66, 35101 (2002)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)
Watts, D.J.: A simple model of global cascades on random networks. Proceedings of National Academy of Science, USA 99, 5766–5771 (2002)
Watts, D.J., Dodds, P.S.: Influence, networks, and public opinion formation. Journal of Consumer Research 34, 441–458 (2007)
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. SIGKDD Explorations 6, 43–52 (2004)
Domingos, P.: Mining social networks for viral marketing. IEEE Intelligent Systems 20, 80–82 (2005)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp. 137–146 (2003)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. In: Proceedings of the 7th ACM Conference on Electronic Commerce (EC 2006), pp. 228–237 (2006)
Kimura, M., Saito, K., Nakano, R.: Extracting influential nodes for information diffusion on a social network. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 1371–1376 (2007)
Kimura, M., Saito, K., Nakano, R., Motoda, H.: Extracting influential nodes on a social network for information diffusion. In: Data Mining and Knowledge Discovery, vol. 20, pp. 70–97. Springer, Heidelberg (2010)
Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI 2008), pp. 1175–1180 (2008)
Kimura, M., Saito, K., Motoda, H.: Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3(2), Article 9, 9 :1–9 : 23 (2009)
Kimura, M., Saito, K., Motoda, H.: Efficient estimation of influence functions fot SIS model on social networks. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI 2009 (2009)
Saito, K., Kimura, M., Motoda, H.: Discovering influential nodes for sis models in social networks. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS (LNAI), vol. 5808, pp. 302–316. Springer, Heidelberg (2009)
Kimura, M., Saito, K., Nakano, R., Motoda, H.: Finding influential nodes in a social network from information diffusion data. In: Proceedings of the International Workshop on Social Computing and Behavioral Modeling (SBP 2009), pp. 138–145 (2009)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Learning continuous-time information diffusion model for social behavioral data analysis. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS, vol. 5828, pp. 322–337. Springer, Heidelberg (2009)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Behavioral analyses of information diffusion models by observed data of social network. In: Chai, S.-K., Salerno, J.J., Mabry, P.L. (eds.) Advances in Social Computing. LNCS, vol. 6007, pp. 149–158. Springer, Heidelberg (2010)
Goyal, A., Bonchi, F., Lakshhmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web Search and Data Mining, pp. 241–250 (2010)
Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the tenth ACM conference on Electronic Commerce, pp. 325–334 (2009)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), pp. 420–429 (2007)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 199–208 (2009)
Mitzenmacher, M.: A brief history of generative models for power law and lognormal distributions. Internet Mathematics 1, 226–251 (2004)
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Saito, K., Kimura, M., Ohara, K., Motoda, H. (2010). Discovery of Super-Mediators of Information Diffusion in Social Networks. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds) Discovery Science. DS 2010. Lecture Notes in Computer Science(), vol 6332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16184-1_11
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DOI: https://doi.org/10.1007/978-3-642-16184-1_11
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