Preprint
Model-based edge clustering for weighted networks with a noise component
ArXiV.org
Cornell University
03/09/2025
DOI: 10.48550/arxiv.2503.06822
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'' has been developed to facilitate its practical implementation.
Details
- Title: Subtitle
- Model-based edge clustering for weighted networks with a noise component
- Creators
- Haomin Li - University of IowaDaniel K Sewell - University of Iowa
- Resource Type
- Preprint
- Publication Details
- ArXiV.org
- DOI
- 10.48550/arxiv.2503.06822
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 03/09/2025
- Academic Unit
- Biostatistics
- Record Identifier
- 9984799690502771
Metrics
12 Record Views