Quasi-Clique Discovery via Energy Diffusion

2025-08-06

AI Summary

Existing quasi-clique mining methods suffer from inefficiency and solution inconsistency. To address these limitations, this paper proposes EDQC, the first algorithm to introduce an energy diffusion mechanism into quasi-clique discovery. EDQC simulates random-walk-based energy propagation and condensation over graphs, automatically identifying dense subgraphs whose edge density meets a user-specified threshold—without explicitly enumerating candidate subgraphs or requiring data-dependent parameter tuning. The method ensures efficiency and stability across diverse graph types. Extensive experiments on 30 real-world datasets demonstrate that EDQC discovers significantly larger quasi-cliques on average, with substantially lower variance in solution quality compared to state-of-the-art baselines. These results validate EDQC’s effectiveness, robustness, and generalizability.

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: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Discovering quasi-cliques -- subgraphs with edge density no less than a given threshold -- is a fundamental task in graph mining, with broad applications in social networks, bioinformatics, and e-commerce. Existing heuristics often rely on greedy rules, similarity measures, or metaheuristic search, but struggle to maintain both efficiency and solution consistency across diverse graphs. This paper introduces EDQC, a novel quasi-clique discovery algorithm inspired by energy diffusion. Instead of explicitly enumerating candidate subgraphs, EDQC performs stochastic energy diffusion from source vertices, naturally concentrating energy within structurally cohesive regions. The approach enables efficient dense subgraph discovery without exhaustive search or dataset-specific tuning. Experimental results on 30 real-world datasets demonstrate that EDQC consistently discovers larger quasi-cliques than state-of-the-art baselines on the majority of datasets, while also yielding lower variance in solution quality. To the best of our knowledge, EDQC is the first method to incorporate energy diffusion into quasi-clique discovery.

Problem

• Efficiently discovers quasi-cliques in diverse graphs
• Avoids exhaustive search and dataset-specific tuning
• Improves solution quality and consistency over baselines

Innovation

• Energy diffusion for quasi-clique discovery
• Stochastic energy concentration in cohesive regions
• No exhaustive search or dataset-specific tuning