Model-based edge clustering for weighted networks with a noise component

Haomin Li and Daniel K. Sewell

Computational Statistics & Data Analysis, 2025, vol. 209, issue C

Abstract: Clustering is a fundamental task in network analysis, essential for uncovering hidden structures within complex systems. Edge clustering, which focuses on relationships between nodes rather than the nodes themselves, has gained increased attention in recent years. However, existing edge clustering algorithms often overlook the significance of edge weights, which can represent the strength or capacity of connections, and fail to account for noisy edges—connections that obscure the true structure of the network. To address these challenges, the Weighted Edge Clustering Adjusting for Noise (WECAN) model is introduced. This novel algorithm integrates edge weights into the clustering process and includes a noise component that filters out spurious edges. WECAN offers a data-driven approach to distinguishing between meaningful and noisy edges, avoiding the arbitrary thresholding commonly used in network analysis. Its effectiveness is demonstrated through simulation studies and applications to real-world datasets, showing significant improvements over traditional clustering methods. Additionally, the R package “WECAN”1 has been developed to facilitate its practical implementation.

Keywords: Community detection; Edge thresholding; Latent space models; Network analysis; Weighted edges (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:209:y:2025:i:c:s0167947325000489

DOI: 10.1016/j.csda.2025.108172

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