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

Danial Ahmadzadeh, Mehrdad Jalali (), Reza Ghaemi and Maryam Kheirabadi
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Danial Ahmadzadeh: Department of Computer Engineering, Ne.C., Islamic Azad University, Neyshabur 9319975853, Iran
Mehrdad Jalali: Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), Hermann-von Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
Reza Ghaemi: Department of Computer Engineering, Qu.C., Islamic Azad University, Quchan 9479176135, Iran
Maryam Kheirabadi: Department of Computer Engineering, Ne.C., Islamic Azad University, Neyshabur 9319975853, Iran

Future Internet, 2025, vol. 17, issue 4, 1-18

Abstract: 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.

Keywords: community detection; GraphDBSCAN; DBSCAN; outlier removal; K-nearest neighbor; autoencoder; Newman–Girvan clustering; graph analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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