social network engineers

Abstract: 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.

Abstract: Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods suffer a sharp drop in model utility under large deletion ratios (mass deletion), a phenomenon we refer to as catastrophic unlearning. We find that a key cause is the uniform treatment of all deleted samples, which is particularly damaging in graph learning: structural dependencies cause different nodes to play vastly different roles in the learned model, yet existing methods apply the same forgetting operation to the entire forget set. Based on this insight, we propose CUNO, a curriculum-based graph unlearning framework that removes the forget set progressively, ordering samples by their estimated unlearning difficulty across multiple stages. CUNO further employs a distribution-level negative preference optimization (NPO) objective at each curriculum stage that steers the model away from its original behavior on the current forget subset while preserving retained performance. Our theoretical analysis shows that the curriculum design is most beneficial when the forget set spans a wide range of unlearning difficulty, a condition naturally satisfied under mass deletion. Comprehensive experiments confirm that CUNO consistently mitigates catastrophic unlearning: at 20% deletion, it retains 74% of the original utility compared to 26-53% for existing methods, and maintains more than half the original utility even at 50% deletion. Our code is publicly available at https://anonymous.4open.science/r/cuno-D4FF.