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.

该论文提出了一种基于蝴蝶核心社区结构的快速搜索方法，用于在异构图中识别与查询顶点对应的密集相互连接的子图。通过随机游走与重新开始算法和蝴蝶度綜合评估社区内顶点的重要性，并采用更有效的顶点距离更新方法，避免不必要的顶点访问，提高操作效率。通过多个真实世界临时图的广泛实验验证了该解决方案的有效性和效率。

大规模标记图的快速蝴蝶核社区搜索