Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph. However, existing heterogeneous graph-based community search methods need help identifying cross-group communities and suffer from efficiency issues, making them unsuitable for large graphs. This paper presents a fast community search model based on the Butterfly-Core Community (BCC) structure for heterogeneous graphs. The Random Walk with Restart (RWR) algorithm and butterfly degree comprehensively evaluate the importance of vertices within communities, allowing leader vertices to be rapidly updated to maintain cross-group cohesion. Moreover, we devised a more efficient method for updating vertex distances, which minimizes vertex visits and enhances operational efficiency. Extensive experiments on several real-world temporal graphs demonstrate the effectiveness and efficiency of this solution.

翻译：社区搜索（CS）旨在识别图中与查询顶点对应的密集互连子图。然而，现有的基于异构图的社区搜索方法难以有效识别跨群体社区，且存在效率问题，难以适用于大规模图。本文提出了一种基于蝴蝶核社区（BCC）结构的快速社区搜索模型，适用于异构图。通过重启随机游走（RWR）算法与蝴蝶度综合评估社区内顶点的重要性，可快速更新领导顶点以维持跨群体内聚性。此外，我们设计了一种更高效的顶点距离更新方法，该方法最小化顶点访问次数并提升运行效率。在多个真实世界时序图上的大量实验证明了该方案的有效性和高效性。