Research Paper#Network Clustering, Silhouette Score, Community Detection🔬 ResearchAnalyzed: Jan 3, 2026 08:38
Silhouette Score Performance in Network Clustering
Published:Dec 31, 2025 13:02
•1 min read
•ArXiv
Analysis
This paper investigates the effectiveness of the silhouette score, a common metric for evaluating clustering quality, specifically within the context of network community detection. It addresses a gap in understanding how well this score performs in various network scenarios (unweighted, weighted, fully connected) and under different conditions (network size, separation strength, community size imbalance). The study's value lies in providing practical guidance for researchers and practitioners using the silhouette score for network clustering, clarifying its limitations and strengths.
Key Takeaways
- •The silhouette score's performance in network clustering is dependent on network characteristics.
- •It performs well with well-separated and balanced clusters.
- •It can underestimate the number of clusters with imbalance or weak separation.
- •It can overestimate the number of clusters in sparse networks.
- •Provides empirical guidance for using the silhouette score in network clustering.
Reference
“The silhouette score accurately identifies the true number of communities when clusters are well separated and balanced, but it tends to underestimate under strong imbalance or weak separation and to overestimate in sparse networks.”