# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 5 Conclusions  
This study introduced GraphDBSCAN, a robust community detection framework that combines adaptive DBSCAN clustering with an improved Newman-Girvan partitioning strategy. By dynamically optimizing clustering parameters, reducing noise, and improving modularity, GraphDBSCAN consistently outperforms traditional methods like Louvain and MDBNG. Experimental results confirm its scalability and adaptability, making it a valuable tool for social networks, biological systems, and materials science applications.  
Future improvements could include integrating Graph Neural Networks (GNNs) for enhanced feature extraction, adapting GraphDBSCAN for dynamic networks, and leveraging deep learning-based clustering for automated similarity measurements. Additionally, optimizing scalability through parallel computing and distributed frameworks like Apache Spark or Deep Graph Library (DGL) could further extend its usability for large-scale datasets.  
Figure 5: Community structure of Cora dataset: (**a**) Initial Louvain clustering, (**b**) GraphDBSCAN refined clustering, (**c**) MDBNG clustering.  
Figure 6: Community structure of Synthetic dataset: (**a**) Initial Louvain clustering, (**b**) GraphDBSCAN refined clustering, (**c**) MDBNG clustering.  
Furthermore, GraphDBSCAN's adaptability extends to materials science, where it can play a crucial role in structure-property mapping and predictive modeling [12; 30; 31]. Once materials are represented as graphs, the ability to efficiently process large-scale material graphs is key to accelerating material discovery. By utilizing GraphDBSCAN's noise-resistant clustering and automated parameter tuning, researchers can uncover hidden patterns in complex material networks, optimize structure-performance relationships, and facilitate the identification of novel materials. This capability is particularly beneficial in high-throughput materials design, where graph-based methods are increasingly being used to model atomic structures, chemical interactions, and material stability. As material databases grow in size and complexity, GraphDBSCAN's ability to handle large, heterogeneous graphs could significantly advance research in high-entropy alloys, metal-organic frameworks (MOFs), and energy storage materials, driving faster and more efficient material innovations.  
Conceptualization, D.A., M.J., R.G. and M.K.; methodology, D.A., M.J., R.G. and M.K.; software, D.A., M.J. and R.G.; validation, D.A., M.J. and R.G.; formal analysis, D.A., M.J., R.G. and M.K.; investigation, D.A., M.J., R.G. and M.K.; resources, D.A., M.J., R.G. and M.K.; data curation, D.A., M.J., R.G. and M.K.; writing--original draft preparation, D.A., M.J. and R.G.; writing--review and editing, D.A., M.J., R.G. and M.K.; visualization, D.A., M.J., R.G. and M.K.; supervision, M.J., R.G. and M.K.; project administration, M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All codes used in this work are available on a public GitHub repository at [[https://github.com/Mehrdadjalali-AI/GraphDBSCAN](https://github.com/Mehrdadjalali-AI/GraphDBSCAN)]([https://github.com/Mehrdadjalali-AI/GraphDBSCAN](https://github.com/Mehrdadjalali-AI/GraphDBSCAN)) (accessed on 1 March 2025).  
The authors declare no conflict of interest.