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.
@article{arxiv.2401.10642,
title = {Fast Butterfly-Core Community Search For Large Labeled Graphs},
author = {JiaYi Du and Yinghao Wu and Wei Ai and Tao Meng and CanHao Xie and KeQin Li},
journal= {arXiv preprint arXiv:2401.10642},
year = {2024}
}