Tight Sampling in Unbounded Networks

TL;DR

This paper introduces a community-focused snowball sampling method for unbounded networks, emphasizing the inclusion of entire cohesive communities rather than traditional representativeness, with experiments demonstrating its effectiveness.

Contribution

It proposes a novel snowball sampling variant that prioritizes community inclusion over coverage, addressing challenges in sampling large, unbounded networks.

Findings

The method effectively includes entire communities in sampled subnetworks.

Experiments on synthetic networks show the sampling behaves as intended.

The approach offers a new perspective on network sampling strategies.

Abstract

The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to prioritize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.