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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
verfasst von : Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda
Erschienen in: Advances in Machine Learning
Verlag: Springer Berlin Heidelberg
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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.