Abstract
In this paper, we attempt to answer a question ”What does an information diffusion model tell about social network structure?” To this end, we propose a new scheme for empirical study to explore the behavioral characteristics of representative information diffusion models such as the IC (Independent Cascade) model and the LT (Linear Threshold) model on large networks with different community structure. To change community structure, we first construct a GR (Generalized Random) network from an originally observed network. Here GR networks are constructed just by randomly rewiring links of the original network without changing the degree of each node. Then we plot the expected number of influenced nodes based on an information diffusion model with respect to the degree of each information source node. Using large real networks, we empirically found that our proposal scheme uncovered a number of new insights. Most importantly, we show that community structure more strongly affects information diffusion processes of the IC model than those of the LT model. Moreover, by visualizing these networks, we give some evidence that our claims are reasonable.
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References
Adar, E., Adamic, L.: Tracking information epidemics in blogspace. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 207–214 (2005)
Balthrop, J., Forrest, S., Newman, M.E.J., Williampson, M.W.: Technological networks and the spread of computer viruses. Science 304, 527–529 (2004)
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th International World Wide Web Conference, pp. 107–117 (2004)
Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146
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, pp. 1371–1376 (2007)
Kimura, M., Saito, K., Motoda, H.: Minimizing the spread of contamination by blocking links in a network. In: Proceedings of the 23nd AAAI Conference on Artificial Intelligence, pp. 1175–1180 (2008)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)
Newman, M.E.J., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Physical Review E 66, 035101 (2002)
Newman, M.E.J., Park, J.: Why social networks are different from other types of networks. Physical Review E 68, 036122 (2003)
Seidman, S.B.: Network Structure and Minimum Degree. Social Networks 5, 269–287 (1983)
Yamada, T., Saito, K., Ueda, N.: Cross-entropy directed embedding of network data. In: Proceedings of the 20th International Conference on Machine Learning, pp. 832–839 (2003)
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Fushimi, T., Kawazoe, T., Saito, K., Kimura, M., Motoda, H. (2009). What Does an Information Diffusion Model Tell about Social Network Structure?. In: Richards, D., Kang, BH. (eds) Knowledge Acquisition: Approaches, Algorithms and Applications. PKAW 2008. Lecture Notes in Computer Science(), vol 5465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01715-5_11
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DOI: https://doi.org/10.1007/978-3-642-01715-5_11
Publisher Name: Springer, Berlin, Heidelberg
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