Overlapping Community Detection Algorithm Based on Enhanced Label Propagation with Graph Neural Network Optimization

Abstract

The structure of a community is essential for understanding complex networks, yet detecting communities efficiently and accurately remains a significant challenge. Although the label propagation algorithm offers linear-time complexity, it faces issues with low robustness, high randomness, and a tendency to form overly large communities. To overcome these limitations, we propose an Overlapping Community Detection Algorithm based on Enhanced Label Propagation with Graph Neural Network Optimization ELP-GNN . Our approach consists of three phases: first, an enhanced label propagation algorithm is employed to identify initial communities by incorporating core node selection and importance-based propagation second, a Graph Neural Network GNN model is trained on the initial communities to learn node embeddings and optimize the community structures and finally, a fusion strategy is applied to combine the strengths of both methods. We evaluate ELP-GNN on both real-world and synthetic networks, comparing its performance with existing overlapping and non-overlapping community detection algorithms. The experimental results demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in complex network structures with high mixing parameters.