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)
datasets exported to the 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:
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; THGB extensions) and follow the licensing of each original data source. See https://tgb.complexdatalab.com/.