Design of Efficient Sampling Methods on Hybrid Social-Affiliation Networks

TL;DR

This paper introduces efficient sampling methods for large-scale social-affiliation networks by leveraging auxiliary and affiliation graphs to improve sampling efficiency and accuracy.

Contribution

It proposes three novel sampling algorithms that utilize hybrid social-affiliation graphs to enhance sampling efficiency in complex networks.

Findings

Proposed methods outperform traditional sampling in disconnected graphs.

Experimental results show improved estimation accuracy.

Effective on both synthetic and real datasets.

Abstract

Graph sampling via crawling has become increasingly popular and important in the study of measuring various characteristics of large scale complex networks. While powerful, it is known to be challenging when the graph is loosely connected or disconnected which slows down the convergence of random walks and can cause poor estimation accuracy. In this work, we observe that the graph under study, or called target graph, usually does not exist in isolation. In many situations, the target graph is related to an auxiliary graph and an affiliation graph, and the target graph becomes well connected when we view it from the perspective of these three graphs together, or called a hybrid social-affiliation graph in this paper. When directly sampling the target graph is difficult or inefficient, we can indirectly sample it efficiently with the assistances of the other two graphs. We design three sampling methods on such a hybrid social-affiliation network. Experiments conducted on both synthetic and real datasets demonstrate the effectiveness of our proposed methods.