An Overlap Community Propagation Algorithm based on Topics and Community Structure in Social networks

Detecting and analyzing community structures is a complex process in social networks. Existing methods of community detection in social networks do not consider both the contents and network structure simultaneously. Users and posts are significant subjects, as they provide wealthy information about social network characteristics, including topics that are of interest to users (topic information) and interactions between users (structure information). Based on the classical Community Overlap Propagation Algorithm (COPRA), we propose an improved Community Overlap Propagation Algorithm based on the Topics and Structure (COPRA-TS), which exploits a comprehensive similarity, and avoids randomness during the label information update process. The proposed COPRA-TS algorithm efficiently filters low-quality data, detects topics of users, computes the topic and structure similarity and utilizes an optimized label information update strategy. Experiments conducted on diverse networks demonstrate that our proposed COPRA-TS algorithm delivers high quality community detection than three notable community detection algorithms.