Bird flock effect-based dynamic community detection: Unravelling network patterns over time

Community structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging technique that requires parameter specification and optimization for quality result. This study proposed an eco-system conceptual framework based on bird flock effect. Relying on the natural law of rule, we designed a dynamic community detection named DCDBFE. The design of algorithm was based on the three basic rules of bird flock: separation, alignment, and cohesion phase. Then, we provide an explanation of similarity measure used between vertices to identify the modules attraction. DCDBFE employs an incremental community detection approach to repeatedly detect communities in each network snapshot or time step. The contributions are obtained for high quality community detected, free-parameter and well stability. To test its performance, extensive experiments were conducted on both synthetic and real-world networks. The outcomes demonstrate that our approach can effectively find satisfaction from each time step by comparison with the other well-known algorithms.