Community detection in networks: Structural communities versus ground truth
This challenges the assumption that structural communities correspond to functional groups, potentially requiring revisions in community modeling for researchers in network science.
The study found that traditional community detection algorithms, which rely solely on network structure, often fail to identify metadata-based groups in large networks, revealing a significant gap between structural communities and ground-truth classifications.
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.