Community detection in networks: Structural communities versus ground truth

TL;DR

This paper investigates whether structural communities detected in networks align with actual node groups based on metadata, revealing a significant disconnect and questioning the effectiveness of current community detection methods.

Contribution

The study provides empirical evidence that traditional structural community detection methods often fail to recover true node groups defined by metadata.

Findings

Structural communities often do not match metadata groups

Traditional algorithms struggle with large networks

Results suggest a need to revise community detection models

Abstract

Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.