Modularity Maximization

Modularity maximization aims to partition nodes in a network into communities by optimizing a metric reflecting the density of within-community connections versus random connections. Current research focuses on improving the efficiency and accuracy of modularity-based algorithms, including heuristic approaches, graph neural networks, and approximation algorithms like the Bayan algorithm, to address limitations in finding optimal or near-optimal community structures, especially in large or dynamic networks. These advancements are significant for various applications, such as social network analysis, recommendation systems, and bioinformatics, where accurate community detection is crucial for understanding complex systems. The development of more scalable and robust methods is a key area of ongoing investigation.