# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 4 Evaluation
### Visualization of Graph Structures and Community Evolution  
To evaluate the impact of GraphDBSCAN on community structure and noise reduction, we present a series of graph visualizations for different datasets. These visualizations illustrate the evolution of community structures by comparing the initial graph before noise removal, the refined structure after applying GraphDBSCAN, and the output of a baseline method for comparison. The key objective is to highlight how GraphDBSCAN improves modularity, refines community separation, and removes noise more effectively than traditional methods.  
Graph-based structural observations demonstrate that GraphDBSCAN significantly enhances community definition by optimizing modularity while preventing excessive fragmentation. The results across multiple datasets show that GraphDBSCAN effectively reorganizes network topology, ensuring that clusters remain well-defined while eliminating weakly connected nodes and outliers. The visualizations in Figures 3-6 illustrate this effect across different datasets, where:  
1. The initial state represents the baseline Louvain clustering before noise removal.
2. The refined state after GraphDBSCAN processing highlights the improved clustering structure.  
Figure 2: Modularity comparison of community detection methods across datasets, highlighting GraphDBSCAN’s superior performance.  
3. The third method (e.g., MDBNG or Newman-Girvan) provides a comparative visualization of an alternative approach. The DBLP dataset initially exhibits loosely defined clusters with visible scattered noise in (a). GraphDBSCAN (b) significantly improves modularity by increasing the number of communities while reducing density, effectively filtering out weakly connected nodes. The comparison method (c), however, over-merges clusters, resulting in less distinct community separation.  
In the Amazon dataset, GraphDBSCAN demonstrates its ability to refine hierarchical structures. In (a), the initial clustering has high noise and weakly connected nodes. GraphDBSCAN (b) removes outliers and improves cluster coherence, leading to better-defined and modular structures. The third method (c) fails to effectively differentiate communities, maintaining overlapping and less distinct clusters.  
Cora's initial structure (a) is relatively dense, making noise filtering less impactful. GraphDBSCAN (b) maintains modularity while removing weakly connected nodes. The alternative method (c) increases density but over-merges communities, leading to less defined partitions.  
Figure 4: Community structure of Amazon dataset: (**a**) Initial Louvain clustering, (**b**) GraphDBSCAN refined clustering, (**c**) MDBNG clustering.  
Figure 3: Community structure of DBLP dataset: (**a**) Initial Louvain clustering, (**b**) GraphDBSCAN refined clustering, (**c**) MDBNG clustering.  
In synthetic datasets, GraphDBSCAN demonstrates its ability to preserve meaningful structures while eliminating noise. The baseline clustering (a) shows moderate modularity, while GraphDBSCAN (b) significantly increases modularity and refines community separations. The third method (c) increases density but fails to differentiate clusters effectively.  
The visual comparisons confirm that GraphDBSCAN effectively enhances community structures by refining modularity, reducing noise, and preventing the over-merging of distinct clusters. Across all datasets, GraphDBSCAN consistently achieves better-defined communities than traditional methods, making it a robust and scalable approach for large-scale networks.