GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering

Community detection in complex networks remains a significant challenge due to noise, outliers, and the dependency on predefined clustering parameters. This study introduces GraphDBSCAN, an adaptive community detection framework that integrates an optimized density-based clustering method with an enhanced graph partitioning approach. The proposed method refines clustering accuracy through three key innovations: (1) a K-nearest neighbor (KNN)-based strategy for automatic parameter tuning in density-based clustering, eliminating the need for manual selection; (2) a proximity-based feature extraction technique that enhances node representations while preserving network topology; and (3) an improved edge removal strategy in graph partitioning, incorporating additional centrality measures to refine community structures. GraphDBSCAN is evaluated on real-world and synthetic datasets, demonstrating improvements in modularity, noise reduction, and clustering robustness. Compared to existing methods, GraphDBSCAN consistently enhances structural coherence, reduces sensitivity to outliers, and improves community separation without requiring fixed parameter assumptions. The proposed method offers a scalable, data-driven approach to community detection, making it suitable for large-scale and heterogeneous networks.