发表机构
College of Computer and Information Science, Southwest University; Huazhong University of Science and Technology; Beijing Institute of Technology（西南大学计算机与信息科学学院; 华中科技大学; 北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有二部图社区搜索忽略时序动态、高阶交互及社区质量的问题，提出WCCS问题，通过(α,β,τ)-楔形核和时序楔形传导率度量，结合在线过滤-扩展框架与离线压缩索引，在七个真实数据集上验证了高效性。
AI 中文摘要
二部图广泛用于建模两个不同实体类型之间的复杂交互，在电子商务、学术网络和社会系统等众多实际应用中无处不在。尽管在二部图上的社区搜索取得了显著进展，但大多数先前的工作仅限于静态设置，忽略了现实世界网络中存在的丰富时序动态。此外，现有方法通常采用边中心度量和严格的连续性约束，未能捕获高阶交互以及频繁但非连续的活动。更重要的是，它们往往忽视了内部凝聚性和外部稀疏性这两个关键的社区质量要求，导致无法识别关键节点或包含许多不相关节点。为解决这些难题，我们提出了新颖的楔形传导率社区搜索（WCCS）问题，旨在识别一个依赖于查询的社区，该社区不仅在结构上和时间上具有凝聚性，而且在非连续时间戳上与网络的其余部分良好分离。我们通过将经典的(α,β)-核推广到高阶(α,β,τ)-楔形核，并提出一种新颖的时间楔形传导率度量来正式定义WCCS，该度量明确平衡了内部密度和外部稀疏性。为高效求解WCCS，我们首先开发了一种在线优先级驱动的过滤-扩展框架，其中包含几种有效的剪枝技术和一种强大的几何斜率优化方法，用于快速计算时间楔形传导率。随后，为进一步提高可扩展性，我们提出了一种离线压缩索引来加速搜索。最后，在七个真实世界数据集上的全面实验证明了我们的解决方案相对于八个竞争对手的有效性、效率和可扩展性。
英文摘要
Bipartite graphs are ubiquitous for modeling complex interactions between two distinct entity types across numerous practical applications such as e-commerce, academic networks, and social systems. Despite significant progress in community search over bipartite graphs, most prior work is limited to static settings and ignores the rich temporal dynamics present in real-world networks. Moreover, existing methods typically adopt edge-centric measures and strict consecutivity constraints, failing to capture higher-order interactions and frequent yet non-consecutive activities. More importantly, they often neglect the crucial community-quality requirements of both internal cohesiveness and external sparsity, failing to identify critical nodes or including many irrelevant nodes. To address these dilemmas, we propose the novel problem of \emph{Wedge Conductance Community Search (WCCS)}, which aims to identify a query-dependent community that is not only structurally and temporally cohesive but also well-separated from the rest of the network over non-consecutive timestamps. We formalize WCCS by generalizing the classical $(α,β)$-core to a higher-order $(α,β,τ)$-wedge core, and by proposing a novel temporal wedge conductance metric that explicitly balances internal density and external sparsity. To solve WCCS efficiently, we first develop an online priority-driven filter-and-expand framework with several effective pruning techniques and a powerful geometric slope optimization for rapid temporal wedge conductance calculation. Subsequently, to further improve scalability, we propose an offline compressed index to accelerate search. Finally, comprehensive experiments on seven real-world datasets demonstrate the effectiveness, efficiency, and scalability of our solutions compared to eight competitors.
