# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 2 Related Works
While these methods excel in integrating diverse data sources, they often assume predefined cluster counts and lack explicit graph structure awareness, limiting their direct applicability to graph-based community detection. Density-based clustering methods have gained attention for their ability to detect arbitrarily shaped clusters without requiring a predefined number of communities. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [10]: DBSCAN is well known for its ability to detect clusters of varying densities while simultaneously identifying noise and outliers. Unlike modularity-based approaches, it does not require a fixed number of clusters and is robust against noise. However, DBSCAN's major limitation is its sensitivity to parameter selection (i.e., Eps (neighborhood radius) and minPts (minimum points per cluster)). In graph-based applications, determining an optimal Eps value is particularly challenging, often requiring manual tuning. MDBNG (Modified Density-Based Network Clustering) [10]: MDBNG is a graph-specific adaptation of DBSCAN, incorporating density estimation heuristics to optimize clustering in social networks and citation graphs. It improves upon DBSCAN by reducing noise sensitivity and introducing a more flexible distance metric for graph structures. However, its performance is inconsistent across datasets, particularly in large, sparse graphs, where over-consolidation of clusters may occur. Additionally, MDBNG still requires manual parameter tuning, making it less adaptable for diverse network structures. To overcome the limitations of modularity-based and density-based methods, recent research has focused on hybrid approaches, which integrate graph partitioning with adaptive clustering to improve scalability, noise resilience, and automatic parameter tuning. Unlike traditional DBSCAN, GraphDBSCAN automatically determines optimal Eps values by leveraging K-nearest neighbor (KNN) optimization, eliminating the need for manual tuning. Additionally, GraphDBSCAN refines community detection using an improved Newman-Girvan edge removal strategy, which selectively removes high-betweenness edges without disrupting core structures. Another key enhancement is proximity-based feature extraction, which integrates autoencoder-driven node embeddings to preserve global network topology while identifying high-density clusters. This multi-step approach significantly improves modularity and clustering accuracy compared to traditional methods. Despite significant progress in community detection, existing methods still face fundamental limitations. Scalability remains a major challenge, as modularity-based approaches such as Louvain and Newman-Girvan often struggle with large, real-world graphs due to their computational complexity [21; 22]. These methods are also affected by the resolution limit, which prevents the detection of smaller communities within large networks. In contrast, density-based methods, such as DBSCAN, require costly parameter tuning, limiting their applicability to diverse datasets [23]. Noise sensitivity is another critical issue, particularly in methods like DBSCAN and MDBNG, which are prone to over-clustering and the misclassification of noise, leading to suboptimal community structures [24]. Additionally, most existing approaches lack adaptive parameter tuning, relying on manually predefined values that result in inconsistent clustering performance across different network topologies [25]. Furthermore, structural adaptability remains limited in methods such as spectral clustering and label propagation, which often fail to adjust to dynamic or evolving networks, making them less effective for large-scale, heterogeneous datasets [14]. Recent advancements, such as the Leiden algorithm, have attempted to address some of these limitations by improving the modularity optimization process and ensuring well-connected communities [14].