Overlapping community detection algorithm based on collective influence

Abstract:

With the advent of the era of big data, network structures are becoming more and more complex, and exploring the community structure of complex networks holds great significance in understanding their function and organization mechanisms. Many studies have been conducted for community detection, among which the label propagation algorithm (LPA) has a near linear time complexity and is applicable for large-scale complex networks. However, it has excessive randomness and relatively low accuracy. This paper proposed a collective influence-based label propagation algorithm (CILPA) for discovering overlapping community structures. CILPA introduced collective influence as a global indicator, redefined the node importance by integrating the node’s own information and global network information, and fixed node update order according to node importance to improve the algorithm’s stability. In the label propagation process, a label selection strategy was designed, and the adaptive filtering factor were set to prevent the interference of wrong labels, thereby improving the accuracy and robustness of the algorithm. Finally, experiments were conducted on artificial and real networks with different scales, complexities, and overlap rates. The results show that the modularity and normalized mutual information of CILPA are superior to those of mainstream algorithms such as COPRA and SLPA, with a smaller standard deviation. This indicates that the proposed method possesses both effectiveness and stability in overlapping community detection, providing a reliable method for the analysis of overlapping communities in large-scale complex networks.