Bipartite graphs, modeling relationships between two types of entities, are widely used in practical applications. Community search, a fundamental problem in bipartite graphs, has gained significant attention. However, existing studies focus on measuring structural cohesiveness between vertex sets while either ignoring edge attributes or considering only one-dimensional importance. In this paper, we introduce a novel community model, named edge-attributed skyline community (ESC), which preserves structural cohesiveness and captures the inherent dominance of multi-dimensional edge attributes in bipartite graphs. To search for ESCs, we developed an efficient peeling algorithm that iteratively deletes edges with the minimum attribute in each dimension. Additionally, we devised an expanding algorithm to reduce the search space and speed up the filtering of unpromising vertices using a proven upper bound. Extensive experiments on large-scale real-world datasets demonstrate the efficiency, effectiveness, and scalability of our approach. A case study compared with prior arts demonstrates that our design improves the precision and diversity of results.

翻译：二分图作为建模两类实体间关系的图结构，在实际应用中被广泛采用。社区搜索作为二分图的基础问题，已获得显著关注。然而，现有研究聚焦于衡量顶点集之间的结构凝聚性，却忽略了边属性或仅考虑单一维度的重要性。本文提出了一种名为"边属性天际线社区(ESC)"的新型社区模型，该模型在保留结构凝聚性的同时，能捕捉二分图中多维边属性的内在支配关系。为搜索ESC，我们开发了一种高效的剥离算法，通过迭代删除各维度属性值最小的边来实现。此外，我们设计了一种扩展算法来缩小搜索空间，并利用已证明的上界加速对无希望顶点的过滤。在大规模真实数据集上的广泛实验证明了我们方法的效率、有效性和可扩展性。与现有技术的案例研究表明，我们的设计显著提升了结果的精准度与多样性。