Experimental Analysis and Evaluation of Cohesive Subgraph Discovery

2025-07-14

AI Summary

Existing studies lack systematic, cross-model comparisons of cohesive subgraph discovery methods under diverse network configurations. Method: We propose the first task-oriented evaluation framework, leveraging both synthetic and real-world networks to quantitatively assess mainstream models along three dimensions—efficiency, cohesiveness, and interpretability—via unified experimental protocols and rigorous statistical analysis. Contribution/Results: Our evaluation reveals critical trade-offs and applicability boundaries across sparsity/density and scale (small/large) regimes. Specifically, classical models (e.g., k-core) excel in efficiency and robustness, whereas density-driven approaches achieve superior cohesiveness in high-cohesion tasks; several recent models suffer from overfitting or scalability bottlenecks. This work establishes an empirical benchmark for model selection and advances cohesive subgraph discovery from algorithm-centric design toward task-aware adaptation.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

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: Models for Web evolution
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.

Problem

• Systematic comparison of cohesive subgraph models' performance
• Evaluating models' efficiency and applicability in varied networks
• Balancing interpretability and cohesion for specific analytical needs

Innovation

• Evaluated cohesive subgraph models systematically
• Used synthetic and real-world networks
• Balanced interpretability and cohesion analysis