Sampling Online Social Networks by Random Walk with Indirect Jumps
Junzhou Zhao, Pinghui Wang, John C.S. Lui, Don Towsley, Xiaohong Guan

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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Human Mobility and Location-Based Analysis · Internet Traffic Analysis and Secure E-voting
