Sampling Online Social Networks by Random Walk with Indirect Jumps

TL;DR

This paper introduces novel sampling methods for large social networks that leverage auxiliary and bipartite graphs to improve sampling efficiency without requiring uniform vertex sampling, addressing slow mixing issues.

Contribution

It proposes a new two-layered network approach enabling indirect sampling of target graphs, enhancing sampling efficiency without uniform vertex sampling.

Findings

Effective sampling on synthetic networks

Improved estimation accuracy on real-world networks

Outperforms traditional random walk methods

Abstract

Random walk-based sampling methods are gaining popularity and importance in characterizing large networks. While powerful, they suffer from the slow mixing problem when the graph is loosely connected, which results in poor estimation accuracy. Random walk with jumps (RWwJ) can address the slow mixing problem but it is inapplicable if the graph does not support uniform vertex sampling (UNI). In this work, we develop methods that can efficiently sample a graph without the necessity of UNI but still enjoy the similar benefits as RWwJ. We observe that many graphs under study, called target graphs, do not exist in isolation. In many situations, a target graph is related to an auxiliary graph and a bipartite graph, and they together form a better connected {\em two-layered network structure}. This new viewpoint brings extra benefits to graph sampling: if directly sampling a target graph is difficult, we can sample it indirectly with the assistance of the other two graphs. We propose a series of new graph sampling techniques by exploiting such a two-layered network structure to estimate target graph characteristics. Experiments conducted on both synthetic and real-world networks demonstrate the effectiveness and usefulness of these new techniques.