Multigraph Sampling of Online Social Networks

TL;DR

This paper introduces a multigraph sampling method for online social networks that leverages multiple user relations to improve sampling efficiency and representativeness, especially in disconnected or clustered graphs.

Contribution

It proposes a novel multigraph sampling technique that combines different social relations for more effective and faster network sampling.

Findings

Multigraph sampling achieves more representative samples.

Faster convergence compared to single-relation sampling.

Effective in disconnected or highly clustered graphs.

Abstract

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 (typically friendship). 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 observe that there often exist other relations between OSN users, such as membership in the same group or participation in the same event. We propose to exploit the graphs these relations induce, by performing a random walk on their union multigraph. 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 social networking features. More specifically, we show that multigraph sampling can obtain a representative sample and faster convergence, even when the individual graphs fail, i.e., are disconnected or highly clustered.