# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 4 Evaluation
### Comparative Evaluation of Community Detection Methods
Unlike these baselines, GraphDBSCAN dynamically adjusts clustering parameters, eliminating manual Eps and minPts selection. This ensures consistent performance across datasets, overcoming fixed parameter limitations that often lead to suboptimal results in standard DBSCAN-based methods. The incorporation of proximity-based feature extraction further enhances cluster coherence, making GraphDBSCAN more effective than adjacency-based approaches. The results presented in Tables 2-5 demonstrate that GraphDBSCAN consistently achieves higher modularity compared to baseline methods across various datasets. This trend is further illustrated in Figure 2, which provides a visual comparison of modularity scores for different community detection methods. The figure highlights the superiority of GraphDBSCAN, particularly in datasets with sparse or noisy structures, where traditional methods struggle to maintain well-defined clusters. By effectively combining adaptive parameter tuning, proximity-based feature extraction, and refined edge removal, GraphDBSCAN ensures higher modularity, improved structural cohesion, and enhanced noise resilience, making it a scalable and reliable approach for community detection in complex networks. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Communities** & **Density** & **Modularity** & **Avg Betweenness** & **Avg Degree** & **Avg Closeness** \\ \hline Louvain & 9 & 0.0151 & 0.5574 & 0.0019 & 0.0151 & 0.3363 \\ GraphDBscan & 26 & 0.0178 & 0.7000 & 0.0000 & 0.0178 & 0.3220 \\ MDBNG & 6 & 0.0271 & 0.5618 & 0.0027 & 0.0271 & 0.3430 \\ Newman–Girvan & 7 & 0.0151 & 0.4004 & 0.0019 & 0.0151 & 0.3363 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Graph metrics for com-Cora dataset (1000 nodes). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Communities** & **Density** & **Modularity** & **Avg Betweenness** & **Avg Degree** & **Avg Closeness** \\ \hline Louvain & 18 & 0.0070 & 0.7460 & 0.0031 & 0.0070 & 0.2539 \\ GraphDBScan & 223 & 0.0043 & 0.9213 & 0.0003 & 0.0043 & 0.2393 \\ MDBNG & 12 & 0.0211 & 0.6991 & 0.0057 & 0.0211 & 0.2608 \\ Newman–Girvan & 7 & 0.0070 & 0.5694 & 0.0031 & 0.0070 & 0.2539 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Graph metrics for com-DBLP dataset (1000 nodes). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Communities** & **Density** & **Modularity** & **Avg Betweenness** & **Avg Degree** & **Avg Closeness** \\ \hline Louvain & 13 & 0.0046 & 0.7262 & 0.0042 & 0.0046 & 0.2094 \\ GraphDBScan & 445 & 0.0011 & 0.8641 & 0.0003 & 0.0011 & 0.2066 \\ MDBNG & 15 & 0.0046 & 0.7313 & 0.0042 & 0.0046 & 0.2094 \\ Newman–Girvan & 7 & 0.0046 & 0.6098 & 0.0042 & 0.0046 & 0.2094 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Graph metrics for com-Cora dataset (1000 nodes). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Communities** & **Density** & **Modularity** & **Avg Betweenness** & **Avg Degree** & **Avg Closeness** \\ \hline Louvain & 12 & 0.0080 & 0.3227 & 0.0021 & 0.0080 & 0.3158 \\ GraphDBScan & 49 & 0.0046 & 0.5430 & 0.0008 & 0.0046 & 0.3064 \\ MDBNG & 10 & 0.0163 & 0.3100 & 0.0040 & 0.0163 & 0.3297 \\ Newman–Girvan & 7 & 0.0080 & 0.0142 & 0.0021 & 0.0080 & 0.3158 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Graph metrics for synthetic dataset (1000 nodes). One of the key advantages of GraphDBSCAN is its ability to dynamically adjust clustering parameters, ensuring robust and adaptable community detection across various datasets. This eliminates manual intervention and prevents suboptimal clustering that often arises from fixed parameter selection. Additionally, the use of proximity-based feature extraction ensures higher modularity and better-defined clusters, particularly in sparse and noisy networks. The refined Newman-Girvan edge removal strategy further enhances clustering stability, preventing random or unstable edge deletions that are common in traditional methods.