Abstract
We developed an algorithm to exactly solve an influence maximization problem (MaxInf) for a two-terminal series parallel graph (TTSPG) in the independent cascade model. The class of TTSPGs can be considered as a class wider than that of trees, only for which an efficient exact solver of this problem has been developed so far. Our algorithm calculates candidate node sets in the divide-and-conquer manner keeping the number of them as small as possible by efficiently eliminating unnecessary ones in merge of subproblems’ solutions. Furthermore, we propose a way of converting an arbitrary network to a TTSPG with edges important for propagation to apply our method to real networks. According to our empirical results, our method is significantly faster than the greedy approximation algorithm for MaxInf of a TTSPG. We also demonstrate improvement of solutions by converting to TTSPGs instead of trees using real networks made from DBLP datasets.
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References
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the KDD, pp. 57–66. ACM (2001)
Gabow, H.N., Galil, Z., Spencer, T., Tarjan, R.E.: Efficient algorithms for finding minimum spanning trees in undirected and directed graphs. Combinatorica 6, 109–122 (1986)
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(3), 211–223 (2001)
Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Lappas, T., Terzi, E., Gunopulos, D., Mannila, H.: Finding effectors in social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1059–1068. ACM (2010)
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, pp. 420–429. ACM (2007)
Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)
Valdes, J., Tarjan, R.E., Lawler, E.L.: The recognition of series parallel digraphs. SIAM J. Comput. 11(2), 298–313 (1982)
Acknowledgement
This work was partially supported by JSPS KAKENHI Grant Number 25280079.
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Tabata, K., Nakamura, A., Kudo, M. (2015). An Algorithm for Influence Maximization in a Two-Terminal Series Parallel Graph and its Application to a Real Network. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_23
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DOI: https://doi.org/10.1007/978-3-319-24282-8_23
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