Case studies in network community detection

TL;DR

This paper presents diverse case studies illustrating how community detection in networks is a practical tool across disciplines, emphasizing its role in facilitating further domain-specific research.

Contribution

It provides real-world examples from various fields demonstrating the application and importance of community detection as a step in network data analysis.

Findings

Community detection varies across disciplines and applications.

Case studies highlight the practical utility of community detection.

Community detection should be guided by specific research goals.

Abstract

Community structure describes the organization of a network into subgraphs that contain a prevalence of edges within each subgraph and relatively few edges across boundaries between subgraphs. The development of community-detection methods has occurred across disciplines, with numerous and varied algorithms proposed to find communities. As we present in this Chapter via several case studies, community detection is not just an "end game" unto itself, but rather a step in the analysis of network data which is then useful for furthering research in the disciplinary domain of interest. These case-study examples arise from diverse applications, ranging from social and political science to neuroscience and genetics, and we have chosen them to demonstrate key aspects of community detection and to highlight that community detection, in practice, should be directed by the application at hand.