Update README (auto from push_data.py)

README.md

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+---
+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/>.