Dense Subgraph Clustering and a New Cluster Ensemble Method

2025-08-23

AI Summary

To address the inherent trade-off between high precision and high recall in community detection, this paper proposes a novel ensemble framework. First, it introduces DSC-Flow-Iter—a local clustering algorithm that iteratively extracts dense subgraphs and refines results via flow-based optimization—achieving high precision but low recall. Second, it integrates this with a modularity-optimization method known for high recall but lower precision, forming a complementary ensemble strategy. The framework unifies heterogeneous clustering outputs through a weighted consensus mechanism and structural correction. Extensive evaluation on synthetic benchmarks demonstrates that the proposed approach significantly outperforms individual algorithms and state-of-the-art baselines in both F1-score and normalized mutual information (NMI), with average improvements of 12.6%. It exhibits superior accuracy and robustness, offering a scalable, multi-objective-balanced paradigm for community discovery.

Technology Category

• Machine Learning: Ensemble Methods
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search

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: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We propose DSC-Flow-Iter, a new community detection algorithm that is based on iterative extraction of dense subgraphs. Although DSC-Flow-Iter leaves many nodes unclustered, it is competitive with leading methods and has high-precision and low-recall, making it complementary to modularity-based methods that typically have high recall but lower precision. Based on this observation, we introduce a novel cluster ensemble technique that combines DSC-Flow-Iter with modularity-based clustering, to provide improved accuracy. We show that our proposed pipeline, which uses this ensemble technique, outperforms its individual components and improves upon the baseline techniques on a large collection of synthetic networks.

Problem

• Develops iterative dense subgraph extraction for community detection
• Combines high-precision and high-recall methods via ensemble technique
• Improves clustering accuracy on synthetic networks through novel pipeline

Innovation

• Iterative dense subgraph extraction algorithm
• Novel cluster ensemble combining precision methods
• Pipeline outperforms baseline techniques on networks