Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection
This is an incremental improvement for researchers and practitioners in network analysis, as it enhances an existing algorithm without requiring prior knowledge of community counts.
The paper tackles unsupervised community detection by integrating Graph Neural Networks with the Louvain algorithm, resulting in dynamic adjustment of community numbers and increased accuracy compared to benchmarks.
This paper proposes a novel community detection method that integrates the Louvain algorithm with Graph Neural Networks (GNNs), enabling the discovery of communities without prior knowledge. Compared to most existing solutions, the proposed method does not require prior knowledge of the number of communities. It enhances the Louvain algorithm using node embeddings generated by a GNN to capture richer structural and feature information. Furthermore, it introduces a merging algorithm to refine the results of the enhanced Louvain algorithm, reducing the number of detected communities. To the best of our knowledge, this work is the first one that improves the Louvain algorithm using GNNs for community detection. The improvement of the proposed method was empirically confirmed through an evaluation on real-world datasets. The results demonstrate its ability to dynamically adjust the number of detected communities and increase the detection accuracy in comparison with the benchmark solutions.