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 shenyang.huang@mail.mcgill.ca 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
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.