# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 1 Introduction  
Community detection plays a crucial role in analyzing complex networks, providing valuable insights into the underlying structures of social networks, biological interactions, citation networks, and e-commerce recommendation systems [1, 2]. Identifying cohesive communities within a graph is essential for various applications, including influence propagation, anomaly detection, link prediction, and large-scale data clustering [3, 4]. However, existing community detection methods often struggle with high-dimensional graph structures, sensitivity to noise, and dependency on predefined parameters, limiting their applicability across diverse datasets. Traditional community detection approaches, such as Louvain [5], Girvan-Newman [6], and Label Propagation [7], have demonstrated effectiveness in modularity-based graphpartitioning. However, these methods suffer from several limitations. One of the primary challenges is sensitivity to noise and outliers, as many clustering algorithms do not incorporate explicit noise removal mechanisms, leading to spurious communities and fragmented graph structures [8]. This issue is particularly pronounced in social networks, where noisy user interactions can obscure meaningful community boundaries, as observed in studies leveraging user actions for detection [9]. Additionally, scalability constraints pose significant challenges, particularly in hierarchical clustering methods such as Girvan-Newman, which suffer from computational inefficiencies, rendering them unsuitable for large-scale networks with millions of nodes and edges [3]. Another key limitation is fixed parameter dependency, as methods like DBSCAN (Density-Based Spatial Clustering of Applications with Noise) require manually set parameters (Eps and minPts), which can result in suboptimal clustering when applied to heterogeneous networks [4]. Finally, structural adaptability remains a concern, as graph-based clustering methods primarily rely on edge betweenness or modularity maximization, failing to capture local neighborhood variations or proximity-based similarities [10]. Recent efforts in community detection within specialized graph structures, such as high-entropy alloy interaction networks, further highlight the challenge of adapting to heterogeneous topologies while managing noise [11; 12]. Given these challenges, an adaptive community detection framework is required to effectively mitigate noise, optimize parameter selection, and enhance clustering accuracy across various network structures. Prior work has highlighted the difficulty of detecting communities in social networks with noisy or sparse data, such as when relying on user interaction patterns, underscoring the need for robust noise-handling techniques [13]. To address these limitations, this study introduces GraphDBSCAN, an enhanced graph-based community detection framework that integrates adaptive DBSCAN clustering with an improved Newman-Girvan partitioning strategy. The proposed method introduces three key innovations. First, adaptive DBSCAN parameter optimization is implemented, eliminating the need for manually predefined Eps (\(\epsilon\)) and minPts values by leveraging a K-nearest neighbor (KNN) optimization strategy. This enhances the adaptability of DBSCAN across diverse graph topologies. Second, proximity-based feature extraction is incorporated to preserve structural integrity while eliminating noise. GraphDBSCAN employs autoencoder-driven node embeddings, leveraging proximity-based representations to refine graph clustering quality. Finally, refined edge removal in Newman-Girvan clustering is introduced, where closeness centrality is utilized as an additional edge-removal criterion, mitigating the instability caused by betweenness-based partitions. By integrating density-based clustering with graph partitioning, GraphDBSCAN outperforms traditional methods by improving modularity, reducing noise sensitivity, and maintaining scalability across real-world and synthetic datasets.