FeatureEnhanced Probabilistic Models for Diffusio

Authors: Liaoruo Wang Stefano Ermon John E Hopcroft

Publish Date: 2012/9/23

Volume: , Issue: , Pages: 499-514

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Abstract

Cascading processes such as disease contagion viral marketing and information diffusion are a pervasive phenomenon in many types of networks The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention However a major challenge is that the underlying network is often unknown In this paper we revisit the problem of inferring latent network structure given observations from a diffusion process such as the spread of trending topics in social media We define a family of novel probabilistic models that can explain recurrent cascading behavior and take into account not only the time differences between events but also a richer set of additional features We show that MAP inference is tractable and can therefore scale to very large realworld networks Further we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10month period Experimental results show that our models accurately recover the links of the Twitter network and significantly improve the performance over previous models based entirely on time

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