Embedding-based Silhouette Community Detection
This work addresses the challenge of community detection in networks for researchers and practitioners, offering a widely applicable embedding-based method that can integrate into existing pipelines, though it appears incremental as it builds on prior embedding techniques.
The authors tackled the problem of detecting communities in networks by proposing Silhouette Community Detection (SCD), which clusters node embeddings derived from neighborhoods, and found that it performs comparably or better than state-of-the-art algorithms like InfoMap and Louvain on 234 synthetic networks and a real-life social network.
Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. In this work, we propose Silhouette Community Detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on 234 synthetic networks, as well as on a real-life social network. Even though SCD is not based on any form of modularity optimization, it performs comparably or better than state-of-the-art community detection algorithms, such as the InfoMap and Louvain algorithms. Further, we demonstrate how SCD's outputs can be used along with domain ontologies in semantic subgroup discovery, yielding human-understandable explanations of communities detected in a real-life protein interaction network. Being embedding-based, SCD is widely applicable and can be tested out-of-the-box as part of many existing network learning and exploration pipelines.