CEO: Identifying Overlapping Communities via Construction, Expansion and Optimization

Overlapping communities are ubiquitous in real-world systems. For overlapping community detection, local expansion methods excel in scalability and efficiency yet have poor tolerance to low-quality seeds and communities. Based on our previous work, we introduce a more robust local-expansion-based overlapping community detection algorithm, named CEO, performing Construction, Expansion and Optimization sub-processes. To solve the poor fault tolerance problem, CEO discards low-quality seeds and communities in each sub-process based on optimizing node memberships. CEO was compared to thirteen noted algorithms by examining the performance on five groups of artificial networks and sixteen real-world networks with ground-truth communities. Experimental results showed CEO performs the best in identifying overlapping communities, which verifies the effectiveness of discarding low-quality seeds and communities in solving the poor fault tolerance problem.