Continuous-Time Dynamic Graph Condensation for Temporal Edge Classification

Graph condensation has emerged as a promising technique for distilling large-scale graphs into compact yet informative ones. However, existing approaches primarily focus on static graphs and largely ignore continuous-time dynamic graphs (CTDGs), where graph evolution is represented as asynchronous event streams. Condensing CTDGs is particularly challenging because both structural evolution and temporal dynamics must be preserved under stringent compression constraints. To address this challenge, we propose Continuous-Time Dynamic Graph Condensation (CTDGC), the first graph condensation framework for event-stream continuous-time dynamic graphs and temporal edge classification. CTDGC adopts a learnable selection-based condensation paradigm that assigns each event an importance score and identifies informative events for condensation. To preserve knowledge from the original event stream, CTDGC first trains a lightweight relay model and then optimizes the condensed graph through feature-level and task-level preservation objectives, retaining both temporal representations and task-relevant semantic information. Furthermore, CTDGC employs class-wise top-$k$ event selection to construct condensed graphs. Since the top-$k$ operation is non-differentiable, a Straight-Through Estimator (STE) is introduced to enable end-to-end optimization. This design naturally satisfies arbitrary compression budgets without requiring additional sparsity-inducing constraints. In extensive experiments on multiple benchmarks, CTDGC consistently outperforms compared baselines under various compression ratios and generalizes well across diverse temporal graph learning architectures. Moreover, on several datasets, models trained on CTDGC-condensed graphs achieve performance competitive with those trained on the original full event streams while using only a small fraction of the training events. Our code will be shared publicly upon paper acceptance.