2018/04/11 by Souâad Boudebza, Rémy Cazabet, Boudebza, Souâad +5
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #Data Visualization and Analytics #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1804.03842
openalex publication_date 2018/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Community structure is one of the most prominent features of complex\nnetworks. Community structure detection is of great importance to provide\ninsights into the network structure and functionalities. Most proposals focus\non static networks. However, finding communities in a dynamic network is even\nmore challenging, especially when communities overlap with each other. In this\narticle , we present an online algorithm, called OLCPM, based on clique\npercolation and label propagation methods. OLCPM can detect overlapping\ncommunities and works on temporal networks with a fine granularity. By locally\nupdating the community structure, OLCPM delivers significant improvement in\nrunning time compared with previous clique percolation techniques. The\nexperimental results on both synthetic and real-world networks illustrate the\neffectiveness of the method.\n