# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 4 Evaluation
### Comparative Evaluation of Community Detection Methods  
Community detection in large-scale networks presents several challenges, including scalability, noise resilience, and parameter dependency. To address these issues, GraphDBSCAN integrates optimized DBSCAN clustering with an improved Newman-Girvan edge removal strategy, enhancing clustering accuracy, adaptability, and modularity. The method is evaluated against widely used baseline algorithms, including Louvain, Newman-Girvan, and MDBNG, across multiple real-world and synthetic datasets (Tables 2-5). The results demonstrate that GraphDBSCAN consistently outperforms baseline methods, particularly in terms of higher modularity, reduced noise sensitivity, and improved structural adaptability. Its superiority stems from three key innovations: (1) KNN-tuned DBSCAN parameter selection, which dynamically adjusts Eps and minPts to remove outliers, reducing graph density and improving clustering robustness; (2) proximity-based feature extraction, which captures indirect relationships between nodes, ensuring more cohesive and meaningful community structures; and (3) an improved Newman-Girvan edge removal step, which refines partitions over traditional betweenness-based edge removal, leading to more stable and well-separated clusters. The selection of DBSCAN parameters was optimized across datasets to enhance modularity, density reduction, and outlier filtering, demonstrating GraphDBSCAN's adaptability to heterogeneous network structures. The dynamic adjustment of Eps values revealed distinct clustering behaviors, where the method refined community structures in dense networks while preventing excessive fragmentation in sparse ones. In DBLP, GraphDBSCAN detects significantly more granular communities than baseline methods while improving modularity. The density reduction indicates effective noise removal, ensuring that spurious connections do not distort clustering outcomes. A similar trend is observed in Amazon, where GraphDBSCAN increases the number of detected communities substantially, demonstrating its ability to identify fine-grained structures in co-purchase networks. For the Cora citation network, modularity remains relatively stable across methods, indicating that the dataset's dense connectivity limits the impact of noise removal. However, GraphDBSCAN improves clustering coherence, refining structural separability without introducing excessive fragmentation. The synthetic dataset serves as a controlled environment to validate GraphDBSCAN's adaptability. The method maintains lower graph density while identifying more distinct communities, confirming its ability to balance structural preservation with effective noise filtering. The modularity improvements indicate that GraphDBSCAN successfully captures latent hierarchical structures, ensuring robustness across both real-world and synthetic networks. GraphDBSCAN is compared with Louvain, Newman-Girvan, and MDBNG as they represent widely used modularity-based and density-based clustering approaches. Each baseline provides distinct benchmarking insights: Louvain serves as a reference for modularity optimization, allowing evaluation of GraphDBSCAN's partitioning efficiency; \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Name** & **Type** & **Nodes** & **Edges** & **Communities** & **Description** \\ \hline com-DBLP & Undirected, Communities & 317,080 & 1,049,866 & 13,477 & DBLP collaboration network \\ com-Amazon & Undirected, Communities & 334,863 & 925,872 & 75,149 & Amazon product network \\ com-Cora & Undirected, Citation & 23,166 & 91,500 & Unknown & Cora citation network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of real-world datasets with node, edge, and community counts. Newman-Girvan assesses the effectiveness of edge removal strategies, providing insights into how GraphDBSCAN improves stability in clustering outcomes; and MDBNG, a DBSCAN-based method, makes a direct comparison possible for evaluating automated parameter tuning and density-aware clustering.