TTGBench: Benchmarking Temporal Interaction Dynamics in Text-attributed Temporal Graphs

Temporal graph learning models dynamic systems as sequences of timestamped interactions, where both structural and semantic dynamics evolve over time. However, existing benchmarks primarily emphasize structural dynamics through temporal link prediction (TLP), while support for semantic dynamics remains limited. Although temporal node classification (TNC) is sometimes included, it is often limited to simple binary settings. Moreover, commonly used datasets exhibit high link repetition, which can obscure whether models truly capture evolving interaction patterns. To address these limitations, we introduce TTGBench, a benchmark for temporal interaction dynamics in text-attributed temporal graphs. TTGBench comprises six real-world, text-rich datasets with high structural novelty and diverse semantic transition patterns, and jointly supports TLP as well as multi-class and multi-label TNC. We conduct a comprehensive evaluation of 17 state-of-the-art methods across Temporal Graph Neural Networks (TGNNs) and Large Language Model (LLM)-based paradigms. The results reveal a clear task-dependent performance asymmetry between the two paradigms: TGNN-based methods excel at structural prediction but fail at semantic dynamics, whereas LLM-based predictors show the opposite trend. Through in-depth analysis, we uncover their fundamental limitations and provide insights for developing more comprehensive temporal graph models. TTGBench datasets, leaderboards, and code are available at our project page https://ttgbench.netlify.app.