Community Detection

Community detection aims to identify groups of densely interconnected nodes within networks, revealing underlying structure and facilitating a deeper understanding of complex systems. Current research emphasizes robust algorithms, including those based on modularity maximization, spectral clustering, graph neural networks, and matrix factorization, often addressing challenges like handling dynamic networks, overlapping communities, and large-scale datasets. These advancements have significant implications for diverse fields, improving analyses of social networks, biological systems, and financial transactions, among others, by providing more accurate and efficient methods for uncovering hidden patterns and relationships.