IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Regular Section
An Analysis of How User Random Walks Influence Information Diffusion in Social Networking Websites
Qian XIAOHaitao XIE
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2015 Volume E98.A Issue 10 Pages 2129-2138

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Abstract

In social websites, users acquire information from adjacent neighbors as well as distant users by seeking along hyperlinks, and therefore, information diffusions, also seen as processes of “user infection”, show both cascading and jumping routes in social networks. Currently, existing analysis suffers from the difficulty in distinguishing between the impacts of information seeking behaviors, i.e. random walks, and other factors leading to user infections. To this end, we present a mechanism to recognize and measure influences of random walks on information diffusions. Firstly, we propose the concept of information propagation structure (IPS), which is also a directed acyclic graph, to represent frequent information diffusion routes in social networks. In IPS, we represent “jumping routes” as virtual arcs and regard them as the traces of random walks. Secondly, we design a frequent IPS mining algorithm (FIPS). By considering descendant node infections as a consequence of ancestor node infections in IPS, we can use a Bayesian network to model each IPS, and learn parameters based on the records of information diffusions passing through the IPS. Finally, we present a quantitative description method of random walks influence, the method is based on Bayesian probabilistic inferring in IPS, which is used to determine the ancestors, whose infection causes the infection of target users. We also employ betweenness centralities of arcs to evaluate contributions of random walks to certain infections. Experiments are carried out with real datasets and simulations. The results show random walks are influential in early and steady phases of information diffusions. They help diffusions pass through some topology limitations in social networks.

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© 2015 The Institute of Electronics, Information and Communication Engineers
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