Research On Collaborative Community Detection Algorithm For Steaming Media Distribution Systems In WMNs

Wireless mesh networks(WMNs) are prone to emulate nodes each other, which makes communities with collaborative capability form in natural evolutionary networks. It is an effective way to improve quality of service(Qo S) on steaming media distribution systems in WMNs by taking advantage of collaborative service capability of communities. In this paper, we propose a community detection algorithm based on the Pearson similarity and the attractive force in the theory of universal gravitation(CDPG) for steaming media distribution systems in WMNs. The experimental results show that CDPG algorithm can detect accurately collaborative community and has a significant improvement in both Qo S and nodes of community churn suppression for steaming media distribution systems in WMNs. With the evolution of society and community structure discovery algorithm running, the entire wireless Mesh streaming media distribution network collaborative community structure will be more clear and obvious, has gradually stabilized. Based on differential Evolution algorithm, the second algorithm DECD(Differential Evolution Community Detection Algorithm) choose the modularity density as the evaluation parameters. Lancichineti benchmark network, Zachery karate club network, university football team network, dolphin network are chosen to prove the effectiveness of the algorithm. Experimental results on both artificial network and real world networks show the algorithm can detect the structure of community complex networks.