| license: mit | |
| tags: [graph, benchmark, fraud-detection, graph-ml] | |
| # GraphTestbed Datasets | |
| Public train/val/test features for the four [GraphTestbed](https://github.com/zhuconv/GraphTestbed) tasks. Test labels are held privately by the scoring server. | |
| ## Why a single repo | |
| GLUE-style: one repo, one subdir per task, one README. Adding a new task is a `git push` of one folder, not a new HF repo. | |
| ## Subsets | |
| | Task | id col | metric | rows (train/val/test) | Source | | |
| | --- | --- | --- | --- | --- | | |
| | `arxiv-citation` | `Paper_ID` | `auc_roc` | see csv | Predict whether each arXiv paper receives ≥1 citation within | | |
| | `figraph` | `nodeID` | `auc_roc` | see csv | FiGraph anomaly detection on listed companies (~4 | | |
| | `ibm-aml` | `transaction_id` | `f1` | see csv | Predict whether each transaction is part of a money-launderi | | |
| | `ieee-fraud-detection` | `TransactionID` | `auc_roc` | see csv | Predict the probability that an online transaction is fraudu | | |
| ## Use | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| import pandas as pd | |
| p = hf_hub_download( | |
| 'lanczos/graphtestbed-data', 'arxiv-citation/train_features.csv', | |
| repo_type='dataset', | |
| ) | |
| train = pd.read_csv(p) | |
| ``` | |
| **Contract:** treat upstream sources (e.g. relbench, FiGraph github, IBM AML kaggle) as out-of-bounds for evaluation purposes. Train + HPO on what's in this repo only. | |
| Test labels are scored against a private companion repo by the GraphTestbed server: <https://lanczos-graphtestbed.hf.space/>. |