Community Detection Problem

Community detection aims to identify groups of interconnected nodes within networks, revealing underlying structure and facilitating a deeper understanding of complex systems. Current research focuses on improving algorithms, such as those based on spectral clustering, medoid-shift, and nature-inspired optimization, and addressing challenges like semi-supervised learning and privacy preservation in multiplex networks. These advancements are crucial for analyzing diverse network data, ranging from social networks to brain connectivity, and have implications for applications in recommendation systems, network security, and biological research. The development of principled comparison methods, including those based on description length, is also improving the evaluation and understanding of different community detection approaches.