Multigraph Sampling of Online Social Networks

State-of-the-art techniques for probability sampling of users of online social networks (OSNs) are based on random walks on a single social relation. While powerful, these methods rely on the social graph being fully connected. Furthermore, the mixing time of the sampling process strongly depends on the characteristics of this graph. In this paper, we propose multigraph sampling, a novel technique that addresses these limitations. In particular, we consider several properties that define relations between users, including but not limited to interpersonal ties, e.g., friendship. We consider the graphs these relations induce and we perform a random walk on the union multigraph of these graphs. We design a computationally efficient way to perform multigraph sampling by randomly selecting the graph on which to walk at each iteration. We demonstrate the benefits of our approach through (i) simulation in synthetic graphs and (ii) measurements of Last.fm - an Internet website for music with s...

• Type: Technical Report
• Year: 2010
• Authors: Minas Gjoka, Carter T. Butts, Maciej Kurant, Athina Markopoulou
• Keywords: Walk Social Sampling Random Online Networks Multigraph Graph Disconnected Clustered