Local Community Detection Algorithm Based on Core Area Expansion

Local community detection is an innovative method to mine cluster structure of extensive networks, that can mine the community of seed node without the need for global structure information about the entire network, as distinct from the global community detection algorithm, it is efficient and costs less. However, a key problem with this field is that the location of seed nodes affects the performance of the algorithm to a great extent, and it is easy to add abnormal nodes to the community, the robustness of the algorithm is low. In this study, we proposed a novel algorithm named CAELCD. First, find the high-quality seed node of the community starting from the initial seed node, so as to avoid the seed-dependent problem. Second, generate the community’s core area and expand to get the local community, which solves the problem that the expansion from a single seed prefers to add the wrong nodes. Experiments on the parameter, accuracy and visualization of the CAELCD are designed on networks with different characteristics. Experimental results demonstrate that CAELCD has superior performance and high robustness.