Due to the ability of modeling relationships between two different types of entities, bipartite graphs are naturally employed in many real-world applications. Community Search in bipartite graphs is a fundamental problem and has gained much attention. However, existing studies focus on measuring the structural cohesiveness between two sets of vertices, while either completely ignoring the edge attributes or only considering one-dimensional importance in forming communities. In this paper, we introduce a novel community model, named edge-attributed skyline community (ESC), which not only preserves the structural cohesiveness but unravels the inherent dominance brought about by multi-dimensional attributes on the edges of bipartite graphs. To search the ESCs, we develop an elegant peeling algorithm by iteratively deleting edges with the minimum attribute in each dimension. In addition, we also devise a more efficient expanding algorithm to further reduce the search space and speed up the filtering of unpromising vertices, where a upper bound is proposed and proven. Extensive experiments on real-world large-scale datasets demonstrate the efficiency, effectiveness, and scalability of the proposed ESC search algorithms. A case study was conducted to compare with existing community models, substantiating that our approach facilitates the precision and diversity of results.

翻译：由于能够建模两种不同类型实体之间的关系，二分图自然被应用于许多实际场景中。二分图中的社区搜索是一个基础问题并已受到广泛关注。然而，现有研究主要关注测量两组顶点之间的结构紧密性，要么完全忽略边属性，要么仅考虑单一维度的重要性来形成社区。本文提出了一种新颖的社区模型——边属性天际线社区（ESC），该模型不仅保留了结构紧密性，还揭示了二分图边上多维属性所固有的支配关系。为搜索ESC，我们开发了一种优雅的剥离算法，通过迭代删除每个维度上属性值最小的边。此外，我们设计了一种更高效的扩展算法来进一步缩减搜索空间并加速过滤无效顶点，其中提出并证明了一个上界。在真实大规模数据集上的大量实验证明了所提出的ESC搜索算法的效率、有效性和可扩展性。通过案例研究对比现有社区模型，验证了我们的方法能够提升结果的精确性和多样性。