  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.
