thgl-github
TGB thgl-github: a large temporal heterogeneous GitHub interaction network with multiple node and edge types. Heterogeneous dynamic link prediction from the Temporal Graph Benchmark.
Schema
Tasks
| task | kind | type | description |
|---|---|---|---|
edge-type-0-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 0; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-1-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 1; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-10-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 10; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-11-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 11; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-12-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 12; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-13-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 13; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-2-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 2; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-3-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 3; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-4-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 4; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-5-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 5; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-6-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 6; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-7-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 7; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-8-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 8; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
edge-type-9-mrr |
external | link_prediction | Heterogeneous next-destination link prediction for edge type 9; ranked against TGB's official negative samples (one-vs-many MRR / Hits@k). |
Loading
import relbench
ds = relbench.load_dataset("relbench/tgb/thgl-github")
task = relbench.load_task("relbench/tgb/thgl-github", "<task>")
Citation
Original dataset: Temporal Graph Benchmark (TGB / TGB 2.0).
@inproceedings{huang2023temporal,
title = {Temporal Graph Benchmark for Machine Learning on Temporal Graphs},
author = {Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael and Rabusseau, Guillaume and Rabbany, Reihaneh},
booktitle = {Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Datasets and Benchmarks Track},
year = {2023}
}
@inproceedings{gastinger2024tgb2,
title = {{TGB 2.0}: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs},
author = {Gastinger, Julia and Huang, Shenyang and Galkin, Mikhail and Loghmani, Erfan and Parviz, Ali and Poursafaei, Farimah and Danovitch, Jacob and Rossi, Emanuele and Koutis, Ioannis and Stuckenschmidt, Heiner and Rabbany, Reihaneh and Rabusseau, Guillaume},
booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
year = {2024}
}
If you use this dataset as hosted by RelBench, please also cite RelBench:
@inproceedings{robinson2024relbench,
title = {{RelBench}: A Benchmark for Deep Learning on Relational Databases},
author = {Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan E. and Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Leskovec, Jure},
booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
year = {2024}
}