在合成数据中测试时序知识图谱模型对分布变化的鲁棒性。
Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation
- 用合成生成器控制时序与结构特性，模拟分布变化。
- 周期性和重复性规律在稳定条件下可恢复，简单记忆模型表现不差。
- 实体社区结构突变最难应对，暴露模型适应缺陷。
时序知识图谱（TKG）表征动态关系系统，其数据生成过程常随时间演变。然而，现有TKG预测模型多在有限的实证基准数据集上评估，难以揭示对分布变化的鲁棒性。为此，我们通过一个合成生成器，在受控环境下研究TKG预测，该生成器编码了三种时序与结构特性——重复性、同质性与周期性，作为数据生成机制。在此基础上，我们在平稳与非平稳条件下评估了七种预测架构。实验表明，模型鲁棒性高度依赖信号类型：在平稳条件下，重复性与周期性规律基本可恢复，简单记忆基线在重复性主导的数据中表现可期；但结构性断裂揭示了模型适应性的局限，尤其是潜在实体-社区结构的变化构成最大挑战。本研究深化了对当前TKG模型在面对时序分布变化时能力与局限的理解。
原文摘要 · Abstract (English)
Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time. Yet, TKG forecasting models are commonly evaluated only on empirical benchmark datasets that provide limited insight into the models' robustness to such distribution shifts. Recognising this issue, we study TKG forecasting under controlled shift environments using a synthetic TKG generator that encodes three temporal and structural properties -- recurrence, homophily, and periodicity -- as data-generating mechanisms. This allows us to evaluate seven forecasting architectures under stationary and shifting regimes. Our experiments suggest that robustness in TKG forecasting is highly signal-dependent. Recurrence-based and periodic regularities are largely recoverable under stationary conditions, and simple memory-based baselines can be competitive when recurrence dominates the data. However, structural breaks reveal limitations in model adaptivity, with shifts in latent entity-community structure posing the strongest challenge in our study. Overall, our findings improve the understanding of the capabilities and limitations of current TKG models confronted with temporal distribution shifts.
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