| --- |
| tags: |
| - relbench |
| - relational-deep-learning |
| - temporal-graph |
| pretty_name: TGB (Temporal Graph Benchmark) in RelBench format |
| configs: |
| - config_name: databases |
| data_files: |
| - split: eval |
| path: STATS/databases.parquet |
| - config_name: tasks |
| data_files: |
| - split: eval |
| path: STATS/tasks.parquet |
| --- |
| |
| # TGB datasets in RelBench format |
|
|
| This repository hosts the [Temporal Graph Benchmark (TGB)](https://tgb.complexdatalab.com/) |
| datasets exported to the [RelBench](https://github.com/snap-stanford/relbench) manifest |
| format. Each dataset lives in its own subdirectory with a self-describing |
| `manifest.yaml`, plain-parquet tables under `db/`, per-task labels under `tasks/<task>/`, |
| and a rendered `schema.svg`. Every task here is `kind: external`: the labels, splits, and |
| official negative samples are produced by TGB and served as-is. |
|
|
| Load any dataset by its subdirectory path: |
|
|
| ```python |
| import relbench |
| ds = relbench.load_dataset("relbench/tgb", revision="<pin>") # via the registry, or: |
| # point directly at a subdir you have locally / downloaded |
| task = relbench.load_task("<dataset>", "<task>") |
| ``` |
|
|
| ## Datasets |
|
|
| ### Dynamic link property prediction (`tgbl-*`) |
| |
| Bipartite or monopartite temporal interaction networks. Task `src-dst-mrr`: predict the |
| next destination for a source, ranked against TGB's official negative samples |
| (one-vs-many MRR / Hits@k). Official `val`/`test` negatives ship under each dataset's |
| `negatives/` directory. |
| |
| | dataset | domain | |
| |---|---| |
| | `tgbl-wiki`, `tgbl-wiki-v2` | Wikipedia editor-page edits | |
| | `tgbl-review`, `tgbl-review-v2` | Amazon electronics user-product reviews | |
| | `tgbl-coin` | cryptocurrency address transactions | |
| | `tgbl-comment` | Reddit reply network | |
| | `tgbl-flight` | global airport-flight network | |
| |
| ### Heterogeneous dynamic link prediction (`thgl-*`) |
|
|
| Temporal heterogeneous graphs with multiple node types (`nodes_type_*`) and edge types |
| (`events_edge_type_*`). One task per edge type (`edge-type-<k>-mrr`), evaluated against |
| official negatives. Global-to-local id `mappings/` and `negatives/` ship with each |
| dataset (required for the TGB evaluation protocol). |
|
|
| | dataset | domain | |
| |---|---| |
| | `thgl-software` | GitHub open-source software interactions | |
| | `thgl-forum` | Reddit forum (users + subreddits) | |
| | `thgl-github` | GitHub interactions (large) | |
| | `thgl-myket` | Myket Android app market interactions | |
|
|
| ### Dynamic node property prediction (`tgbn-*`) |
| |
| Node-affinity prediction over time. Task `node-label-ndcg`: predict the per-node label |
| distribution at the next timestamp, evaluated by NDCG@10. Labels are carried in the |
| `labels` / `label_events` / `label_event_items` tables. |
| |
| | dataset | domain | |
| |---|---| |
| | `tgbn-trade` | UN international agriculture trade | |
| |
| ## Notes on evaluation |
| |
| The RelBench manifest records `evaluator: tgb` on each task. TGB uses a custom protocol |
| (one-vs-many MRR / Hits@k for link tasks, NDCG@10 for node tasks) that differs from |
| RelBench's default link/classification metrics. The official negative samples and id |
| mappings needed to reproduce the TGB numbers are shipped alongside the data. |
| |
| ## Citation |
| |
| If you use these datasets, please cite the TGB papers |
| ([Huang et al., 2023](https://arxiv.org/abs/2307.01026); THGB extensions) and follow the |
| licensing of each original data source. See <https://tgb.complexdatalab.com/>. |
| |
