Community structure detection from networks with weighted modularity
Community detection from networks is an emerging topic in modern network science. Communities are defined as clusters of nodes or vertices that share higher concentration of edges among themselves than sharing with other nodes in the network. Community structure is an important property of real systems and detecting communities enables us to better understand the underlying structure of the system. The most widely used method for community detection is modularity maximization which works by optimizing a quality function named modularity of the network partition. However, traditional modularity-based approaches generally have a resolution limit that prevents them from detecting communities that are sufficiently smaller compared to the whole network. In this work, we target to overcome the resolution limit of the modularity function by incorporating a weight term in the modularity formulation. We propose a community detection approach based on a community quality metric, named as weighted modularity. We validate the performance of the proposed method in several benchmark networks and show that the proposed method is promising in different settings.