arXiv AI September 10, 2026 By Longfei Ma, Zemin Liu, Fei Wu

TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs

TTGBench is a new benchmark for temporal graph learning that evaluates both structural evolution and semantic drift in text‑attributed graphs. It includes six real‑world, text‑rich datasets with dual volatility and supports multi‑class and multi‑label temporal node classification, addressing gaps left by existing benchmarks. A comprehensive evaluation of 17 state‑of‑the‑art methods shows a clear divide: TGNNs excel at structural prediction but struggle with semantic tracking, while LLM‑based models perform better on semantic tasks but lag in structural prediction.

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