# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method  
This paper introduces GraphDBSCAN, a novel framework for community detection that integrates spatial proximity feature extraction, outlier elimination, and an enhanced Newman-Girvan (NG) clustering approach to improve the accuracy and robustness of community detection in networks. Unlike conventional methods that rely solely on adjacency matrices, GraphDBSCAN leverages a proximity-based representation that captures both direct and indirect relationships between nodes, thereby enhancing the effectiveness of clustering. The proposed method employs a multi-step pipeline consisting of proximity feature extraction, dimensionality reduction, adaptive outlier detection using DBSCAN, and a refined community detection process using an improved NG algorithm. This workflow is visually summarized in Figure 1.  
As depicted in Figure 1, the process begins with the input of social network data, which undergoes proximity-based feature extraction to construct a spatial proximity matrix. Unlike traditional adjacency matrices, this representation encodes both explicit and implicit node interactions, providing a more comprehensive network structure for subsequent analysis. The proximity-based representation is further refined through an autoencoder-driven dimensionality reduction technique, which compresses the high-dimensional feature  
Figure 1: Proposed architecture for GraphDBSCAN, integrating feature extraction, adaptive DBSCAN, outlier detection, and improved Newman–Girvan clustering. space into a more compact and informative representation while mitigating noise and preserving the nonlinear structure of the network. Following this preprocessing step, GraphDBSCAN employs an adaptive DBSCAN clustering approach to detect and remove outliers, ensuring that community detection is applied to a cleaner dataset. A key limitation of previous methods, such as in [26], is that DBSCAN requires manually defined bestEps and minPts parameters, making results highly sensitive to user input. To overcome this, Algorithm 1 introduces a KNN-based parameter tuning mechanism that dynamically determines the optimal bestEps and minPts values based on local density estimations, thereby improving the reliability and robustness of outlier detection. This approach eliminates user dependency and enhances clustering stability, making the outlier detection process adaptive and data-driven. Once outliers are identified and removed, the refined dataset undergoes community detection using an improved NG algorithm. Traditional NG-based approaches rely on edge betweenness to iteratively remove high-centrality edges to reveal community structures. However, a critical issue in previous implementations, such as [26], is the random selection of edges for removal when multiple edges have similar betweenness values, leading to inconsistencies in the resulting community partitions. To address this, Algorithm 2 refines the NG clustering step by incorporating closeness centrality in addition to betweenness centrality, ensuring a more deterministic and precise edge removal process. This enhancement allows for a more structured partitioning of communities, reducing randomness and instability in the clustering process. To ensure efficient execution of the entire GraphDBSCAN pipeline, Algorithm 3 orchestrates the sequential execution of feature extraction, outlier detection, and community detection while maintaining computational efficiency. Finally, the detected communities are evaluated using key network metrics, including modularity, density, betweenness centrality, degree centrality, and closeness centrality, as summarized in Algorithm 4. These metrics provide quantitative validation of the effectiveness of GraphDBSCAN in identifying well-structured and cohesive communities. One of the key improvements of GraphDBSCAN over existing methods lies in its ability to dynamically optimize clustering parameters while simultaneously enhancing community partitioning quality.