# Embedding-based Silhouette Community Detection

## TL;DR

This paper introduces Silhouette Community Detection (SCD), a novel embedding-based method for identifying network communities that performs well on synthetic and real-world data, providing interpretable results without modularity optimization.

## Contribution

The paper presents SCD, a new community detection approach using node embeddings, which outperforms or matches existing algorithms and offers human-understandable explanations.

## Key findings

- SCD performs comparably or better than InfoMap and Louvain.
- SCD is effective on synthetic and real-world networks.
- Outputs can be used with ontologies for semantic analysis.

## Abstract

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.