Covariate Connectivity Combined Clustering for Weighted Networks

2025-11-21

AI Summary

Traditional community detection methods rely solely on network topology and thus underperform when node attributes contain community signals; existing covariate-assisted approaches often require pre-specified numbers of clusters, incur high computational costs, and are incompatible with weighted networks. This paper proposes an adaptive spectral clustering framework for weighted networks that jointly models topological connectivity and node covariates. A data-driven mechanism automatically balances their respective contributions, while a spectral gap–based heuristic estimates the number of communities without prior specification or MCMC sampling. The method integrates refined spectral clustering, a joint similarity metric, and adaptive parameter tuning, substantially improving accuracy and robustness. Extensive simulations demonstrate superior performance over state-of-the-art baselines. Empirical evaluation on a real-world airport accessibility network confirms its scalability, interpretability, and practical utility.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Clustering

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Social Networks and Social Media: Computational social science

Abstract

Community detection is a central task in network analysis, with applications in social, biological, and technological systems. Traditional algorithms rely primarily on network topology, which can fail when community signals are partly encoded in node-specific attributes. Existing covariate-assisted methods often assume the number of clusters is known, involve computationally intensive inference, or are not designed for weighted networks. We propose $ ext{C}^4$: Covariate Connectivity Combined Clustering, an adaptive spectral clustering algorithm that integrates network connectivity and node-level covariates into a unified similarity representation. $ ext{C}^4$ balances the two sources of information through a data-driven tuning parameter, estimates the number of communities via an eigengap heuristic, and avoids reliance on costly sampling-based procedures. Simulation studies show that $ ext{C}^4$ achieves higher accuracy and robustness than competing approaches across diverse scenarios. Application to an airport reachability network demonstrates the method's scalability, interpretability, and practical utility for real-world weighted networks.

Problem

• Detects communities in weighted networks using node attributes and topology
• Overcomes limitations of methods requiring known cluster counts or intensive computation
• Provides automated community estimation without sampling-based inference procedures

Innovation

• Integrates network connectivity and node-level covariates
• Balances information sources with data-driven tuning parameter
• Estimates community count via eigengap heuristic method