Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis

Authors:
Kazumi Saito;Masahiro Kimura;Kouzou Ohara;Hiroshi Motoda
Affiliations:
School of Administration and Informatics, University of Shizuoka, Shizuoka, Japan 422-8526;Department of Electronics and Informatics, Ryukoku University, Otsu, Japan 520-2194;Department of Integrated Information Technology, Aoyama Gakuin Univesity, Kanagawa, Japan 229-8558;Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan 567-0047
Venue:
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Year:
2009

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Abstract

We address the problem of estimating the parameters for a continuous time delay independent cascade (CTIC) model, a more realistic model for information diffusion in complex social network, from the observed information diffusion data. For this purpose we formulate the rigorous likelihood to obtain the observed data and propose an iterative method to obtain the parameters (time-delay and diffusion) by maximizing this likelihood. We apply this method first to the problem of ranking influential nodes using the network structure taken from two real world web datasets and show that the proposed method can predict the high ranked influential nodes much more accurately than the well studied conventional four heuristic methods, and second to the problem of evaluating how different topics propagate in different ways using a real world blog data and show that there are indeed differences in the propagation speed among different topics.