无需参数的动态图表示学习，精准捕捉社区间时序演化模式。
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs
- 基于社区的无参采样机制，自适应生成时序路径。
- 在多个基准数据集上链接预测准确率超越现有方法。
- 适合研究动态社交网络、金融交易等连续时间演化场景。
动态图表示学习在理解行为演化中具有关键作用。然而，现有方法常面临灵活性差、适应性弱及难以保留时空动态的问题。为此，我们提出社区感知时序游走（CTWalks），一种面向连续时间动态图的新型表示学习框架。CTWalks融合三大核心组件：基于社区的无参时序游走采样机制、融入社区标签的匿名化策略，以及利用常微分方程（ODE）建模连续时态动态的编码过程。该设计可精确刻画社区内与社区间的交互，实现对连续时间动态图中演化模式的细粒度表征。理论上，CTWalks克服了游走中的局部性偏差，并建立了与矩阵分解的联系。在基准数据集上的实验表明，其在时序链接预测任务中优于已有方法，具备更高准确率和鲁棒性。
原文摘要 · Abstract (English)
Dynamic graph representation learning plays a crucial role in understanding evolving behaviors. However, existing methods often struggle with flexibility, adaptability, and the preservation of temporal and structural dynamics. To address these issues, we propose Community-aware Temporal Walks (CTWalks), a novel framework for representation learning on continuous-time dynamic graphs. CTWalks integrates three key components: a community-based parameter-free temporal walk sampling mechanism, an anonymization strategy enriched with community labels, and an encoding process that leverages continuous temporal dynamics modeled via ordinary differential equations (ODEs). This design enables precise modeling of both intra- and inter-community interactions, offering a fine-grained representation of evolving temporal patterns in continuous-time dynamic graphs. CTWalks theoretically overcomes locality bias in walks and establishes its connection to matrix factorization. Experiments on benchmark datasets demonstrate that CTWalks outperforms established methods in temporal link prediction tasks, achieving higher accuracy while maintaining robustness.
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