What Does an Information Diffusion Model Tell about Social Network Structure?

Authors:
Takayasu Fushimi;Takashi Kawazoe;Kazumi Saito;Masahiro Kimura;Hiroshi Motoda
Affiliations:
School of Administration and Informatics, University of Shizuoka, Shizuoka, Japan 422-8526;School of Administration and Informatics, University of Shizuoka, Shizuoka, Japan 422-8526;School of Administration and Informatics, University of Shizuoka, Shizuoka, Japan 422-8526;Department of Electronics and Informatics, Ryukoku University Otsu, Shiga, Japan 520-2194;Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan 567-0047
Venue:
Knowledge Acquisition: Approaches, Algorithms and Applications
Year:
2009

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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.