Compared to global community detection, local community detection aims to find communities that contain a given node. Therefore, it can be regarded as a specific and personalized community detection task. Local community detection algorithms based on modularity are widely studied and applied because of their concise strategies and prominent effects. However, they also face challenges, such as sensitivity to seed node selection and unstable communities. In this paper, a local community detection algorithm based on local modularity density is proposed. The algorithm divides the formation process of local communities into a core area detection stage and a local community extension stage according to community tightness based on the Jaccard coefficient. In the core area detection stage, the modularity density is used to ensure the quality of the communities. In the local community extension stage, the influence of nodes and the similarity between the nodes and the local community are utilized to determine boundary nodes to reduce the sensitivity to seed node selection. Experimental results on real and artificial networks demonstrated that the proposed algorithm can detect local communities with high accuracy and stability.

与全局社区检测相比，本地社区检测旨在查找包含给定节点的社区。因此，可以将其视为特定且个性化的社区检测任务。基于模块化的局部社区检测算法由于其简洁的策略和突出的效果而得到了广泛的研究和应用。但是，他们也面临挑战，例如对种子节点选择的敏感性和不稳定的社区。提出了一种基于局部模块化密度的局部社区检测算法。该算法根据基于Jaccard系数的社区紧密度，将局部社区的形成过程分为核心区域检测阶段和局部社区扩展阶段。在核心区域检测阶段，模块化密度用于确保社区的质量。在本地社区扩展阶段，利用节点的影响以及节点与本地社区之间的相似性来确定边界节点，以降低对种子节点选择的敏感性。在真实和人工网络上的实验结果表明，该算法可以高精度，稳定地检测局部社区。