twitter-research/tgn

Health v2 · maintenance only

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1202 stars · 246 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of Temporal Graph Networks (TGN), a framework for deep learning on dynamic graphs represented as sequences of timed events. It includes training scripts for self-supervised link prediction and supervised node classification, plus baseline comparisons on datasets like Wikipedia and Reddit.

Use cases

• implement temporal graph networks for dynamic graph learning
• run link prediction on timestamped interaction datasets
• train models on dynamic node classification tasks
• reproduce TGN paper experiments and baselines
• compare memory-based models on dynamic graphs
• benchmark deep learning models on JODIE datasets

When to choose

• you need state-of-the-art models for learning on dynamic or temporal graphs
• you want to reproduce or extend the TGN paper's results
• your data is a sequence of timed events or interactions between nodes
• you need a research baseline for temporal link prediction

When to avoid

• you need a production-ready, well-maintained library with long-term support
• you work with static graphs rather than time-stamped event sequences
• you need a high-level API or integration with modern graph frameworks like PyTorch Geometric or DGL
• you require recent PyTorch versions, since the code targets older dependencies

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning data-science python temporal-graphs dynamic-graphs graph-neural-networks research-code pytorch link-prediction algorithms

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