构建合成基准测试，评估图神经网络对时序模式的推理能力
T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
- 设计可控合成任务，分离检测周期性、因果延迟和长程依赖
- 11种模型在任务中表现不佳，暴露出泛化能力缺陷
- 适合研究时序图学习、模型可解释性与基准测试的学者
动态图学习方法近年来成为建模随时间演化的关系数据的强大工具。然而，尽管已有大量基准测试，当前时序图神经网络（TGNNs）是否真正掌握周期性、因果关系和长程依赖等核心时序模式仍不明确。本文提出时序图推理基准（T-GRAB），一套系统化的合成任务，用于严格检验TGNNs的时间推理能力。T-GRAB提供可控且可解释的任务，分别聚焦于计数/记忆周期重复、推断延迟因果效应，以及跨时空维度的长程依赖捕捉。我们在11种时序图学习方法上进行评估，揭示其在泛化时序模式方面存在根本性不足。研究结果为当前模型局限提供了可操作的洞察，指出了传统真实世界基准难以暴露的问题，并推动具备更强时序推理能力的架构发展。代码已开源：https://github.com/alirezadizaji/T-GRAB。
原文摘要 · Abstract (English)
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite extensive benchmarking efforts, it remains unclear whether current Temporal Graph Neural Networks (TGNNs) effectively capture core temporal patterns such as periodicity, cause-and-effect, and long-range dependencies. In this work, we introduce the Temporal Graph Reasoning Benchmark (T-GRAB), a comprehensive set of synthetic tasks designed to systematically probe the capabilities of TGNNs to reason across time. T-GRAB provides controlled, interpretable tasks that isolate key temporal skills: counting/memorizing periodic repetitions, inferring delayed causal effects, and capturing long-range dependencies over both spatial and temporal dimensions. We evaluate 11 temporal graph learning methods on these tasks, revealing fundamental shortcomings in their ability to generalize temporal patterns. Our findings offer actionable insights into the limitations of current models, highlight challenges hidden by traditional real-world benchmarks, and motivate the development of architectures with stronger temporal reasoning abilities. The code for T-GRAB can be found at: https://github.com/alirezadizaji/T-GRAB.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。