ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
| Hi-index | 0.00 |
Most social networks exhibit community structures, in which nodes are tightly connected to each other within acommunity but only loosely connected to nodes in other communities. Researches on community mining have received a lot of attention; however, most of them are based on a centralized system model and thus not applicable tothe distributed model of P2P networks. In this paper, we propose a distributed community mining algorithm, namely Asynchronous Clustering and Merging scheme (ACM), for computing environments. Due to the dynamic and distributed nature of P2P networks, The ACM scheme employs an asynchronous strategy such that local clustering is executed without requiring an expensive global clustering to be performed in a synchronous fashion. Experimental results show that ACM is able to discover community structures with high quality while outperforming the existing approaches.