When can networks be inferred from observed groups?

TL;DR

This paper investigates when and how unobserved networks can be accurately inferred from observed group data, highlighting the effectiveness of simple projections for few groups and statistical models for many groups.

Contribution

It provides a systematic analysis of inference methods for unobserved networks from group observations, offering practical guidance based on the number of groups and their structure.

Findings

Simple unweighted two-mode projection works well with few groups and clique-like memberships.

Statistical backbone extraction accurately infers networks with many observed groups, even if memberships are random.

Guides researchers on choosing inference methods based on data characteristics.

Abstract

Collecting network data directly from network members can be challenging. One alternative involves inferring a network from observed groups, for example, inferring a network of scientific collaboration from researchers' observed paper authorships. In this paper, I explore when an unobserved undirected network of interest can accurately be inferred from observed groups. The analysis uses simulations to experimentally manipulate the structure of the unobserved network to be inferred, the number of groups observed, the extent to which the observed groups correspond to cliques in the unobserved network, and the method used to draw inferences. I find that when a small number of groups are observed, an unobserved network can be accurately inferred using a simple unweighted two-mode projection, provided that each group's membership closely corresponds to a clique in the unobserved network. In contrast, when a large number of groups are observed, an unobserved network can be accurately inferred using a statistical backbone extraction model, even if the groups' memberships are mostly random. These findings offer guidance for researchers seeking to indirectly measure a network of interest using observations of groups.