Sampling promotes community structure in social and information networks

TL;DR

This paper investigates how different sampling techniques affect the perceived community structures in social and information networks, revealing that sampling often artificially enhances community-like group detection.

Contribution

It demonstrates that sampling methods can distort network structures, making community-like groups appear more prominent than in the original networks.

Findings

Sampled social networks show densely linked communities.

Sampled information networks highlight modules of equivalent nodes.

Sampling amplifies community-like structures regardless of network type.

Abstract

Any network studied in the literature is inevitably just a sampled representative of its real-world analogue. Additionally, network sampling is lately often applied to large networks to allow for their faster and more efficient analysis. Nevertheless, the changes in network structure introduced by sampling are still far from understood. In this paper, we study the presence of characteristic groups of nodes in sampled social and information networks. We consider different network sampling techniques including random node and link selection, network exploration and expansion. We first observe that the structure of social networks reveals densely linked groups like communities, while the structure of information networks is better described by modules of structurally equivalent nodes. However, despite these notable differences, the structure of sampled networks exhibits stronger characterization by community-like groups than the original networks, irrespective of their type and consistently across various sampling techniques. Hence, rich community structure commonly observed in social and information networks is to some extent merely an artifact of sampling.