A fast local community detection algorithm in complex networks

Considering the problems of communication overhead between nodes and the challenges brought by large-scale complex network in distributed system cluster management, we propose a local community detection algorithm in complex network called FLCDA (Fast Local Community Detection Algorithm). FLCDA can detect communities in a large-scale complex network on a single PC. The algorithm uses a Parallel Sliding Windows (PSW) method to break a large-scale network into smaller sub-networks, and load sub-network into a PC memory. This method conforms to the local characteristics of the community. FLCDA first finds out disjoint Maximum Influence k-clique in each sub-network, and then assigns the same label and weight to all nodes in the same Maximum Influence k-clique. These labels and weights are used as seeds at the label propagation phase of FLCDA. During the labeling propagation phase, FLCDA applies a synchronous update strategy while removes meaningless labels after each iteration. When all node labels are updated, the update process will be ended. This method can reduce the calculation cost and improve the stability. The experiment results show that non-parameter FLCDA can self-adaptively detect communities on various scales and types of complex networks. By comparing with other algorithms, FLCDA has gained higher community detection accuracy and less running time. Therefore, FLCDA algorithm can be better adapted to community detection in real complex networks without prior knowledge.