TGB Dataset Overview
The Temporal Graph Benchmark~(TGB) aims to provide datasets and evaluation protocols for realistic, reproducible, and robust evaluation for machine learning on temporal graphs.
Edge and Node-level tasks: We include both the dynamic link property prediction task and the dynamic node property prediction task
Rich domains: TGB datasets come from interaction networks, rating networks, trasanction networks, traffic networks, social networks and trade networks.
Diverse in scale: TGB datasets includes small (< 5 million edges), medium (< 25 million edges) and large (> 25 million edges)scale datasets
Leaderboard Submission
To submit to TGB datasets, please fill in the following google form and reach out to [email protected] if you have any questions. All results should be reported across 5 runs for both validation and test performance. Rules for the Leaderboard is found here.
Dataset splits
All datasets are split chronologically into the train, validation and test set with 70%, 15% and 15% of the edges respectively
Exploring datasets
You can plot temporal graph statistics or visualizations in our companion Python package: TGX. TGX is a Python library designed to analyze and visualize temporal graphs. All TGB datasets are directly supported in TGX, see our example tutorial here.
Contributing datasets
TGB welcomes community feedback and contributions, if you would like to contribute a datasets or raise an issue, please reach out by email.