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Random Walks in Social Networks and their Applications: A Survey

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Social Network Data Analytics

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Abstract

A wide variety of interesting real world applications, e.g. friend suggestion in social networks, keyword search in databases, web-spam detection etc. can be framed as ranking entities in a graph. In order to obtain ranking we need a graph-theoretic measure of similarity. Ideally this should capture the information hidden in the graph structure. For example, two entities are similar, if there are lots of short paths between them. Random walks have proven to be a simple, yet powerful mathematical tool for extracting information from the ensemble of paths between entities in a graph. Since real world graphs are enormous and complex, ranking using random walks is still an active area of research. The research in this area spans from new applications to novel algorithms and mathematical analysis, bringing together ideas from different branches of statistics, mathematics and computer science. In this book chapter, we describe different random walk based proximity measures, their applications, and existing algorithms for computing them.

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Authors and Affiliations

  1. Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Purnamrita Sarkar

  2. Google, Inc., Pittsburgh, PA, USA

    Andrew W. Moore

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  1. Purnamrita Sarkar
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Correspondence to Purnamrita Sarkar .

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  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, New York, USA

    Charu C. Aggarwal

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Sarkar, P., Moore, A.W. (2011). Random Walks in Social Networks and their Applications: A Survey. In: Aggarwal, C. (eds) Social Network Data Analytics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8462-3_3

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