Analyzing Overlapping and Non-overlapping Communities in Complex Networks

Understanding the fundamental structure and behavior of linked systems depends on knowing community detection in complicated networks. The study of overlapping and non-overlapping communities in complex networks is investigated here with an emphasis on their importance in exposing latent trends and relationships. Particularly important in social, biological, and technical networks, overlapping communities—where nodes might belong to several clusters—reflect the complex character of real-world interactions. On the other hand, non-overlapping communities have unambiguous limits that help to understand network components easily. This work compares the efficiency of modern techniques including Clique Percolation Method (CPM), Label Propagation, and modularity optimization in identifying overlapping and non-overlapping communities. Furthermore, the study investigates how community organization affects network robustness and functionality. This paper assesses the performance, accuracy, and scalability of various algorithms using real-world datasets from social media, biological networks, and citation networks. Furthermore, the study includes the difficulties of community overlap identification, including computational complexity and interpretability concerns. The results offer insightful analysis of choosing suitable community detection methods depending on network properties and application criteria. This work helps to clarify difficult network architectures, enable developments in social network research, bioinformatics, and cybersecurity.