---
license: mit
configs:
- config_name: nodes
  default: true
  data_files:
  - split: train
    path: nodes.parquet
- config_name: edges
  data_files:
  - split: train
    path: edges.parquet
task_categories:
- graph-ml
tags:
- anomaly-detection
- graph-anomaly-detection
- outlier-detection
- fraud-detection
- graph
pretty_name: Questions (graph anomaly detection)
size_categories:
- 10K<n<100K
---

# Questions (graph anomaly detection)

Users of the Yandex Q question-answering service, connected by answering interactions. The minority class marks users by activity outcome; at a 3.0% base rate this is a realistic rare-anomaly regime.

|  | |
|---|---|
| Nodes | 48,921 |
| Node features | 301 |
| Edges | 153,540 |
| Outliers | 1,460 (3.0%) |
| Label type | adjudicated |
| Label source | Yandex Q activity outcome, Platonov et al. 2023 |

**Viewer.** Two configs: `nodes` (default) and `edges`.

**Files.** `nodes.parquet` (node_id, `feat_*`, `label`, split masks where available)
and `edges.parquet` (`src`, `dst`, the edge list as shipped upstream; symmetrize for
undirected use). Ships the upstream 10 split trials as `train_mask_0..9`, `val_mask_0..9`, `test_mask_0..9`.

## Load

```python
import pandas as pd

nodes = pd.read_parquet("hf://datasets/JaySuryavanshi/graph-anomaly-questions/nodes.parquet")
edges = pd.read_parquet("hf://datasets/JaySuryavanshi/graph-anomaly-questions/edges.parquet")
```

As a graph, with [graphspot](https://github.com/JayeshSuryavanshi/graphspot)
(`pip install graphspot`):

```python
import numpy as np, scipy.sparse as sp, graphspot
from graphspot.detectors import XGBGraph

n = len(nodes)
adj = sp.csr_matrix((np.ones(len(edges)), (edges.src, edges.dst)), shape=(n, n))
g = graphspot.Graph(adj=adj, x=nodes.filter(like="feat_").to_numpy(),
                    node_labels=nodes.label.to_numpy())
```

## Provenance

Mirrored unmodified (beyond format conversion to parquet) from [yandex-research/heterophilous-graphs](https://github.com/yandex-research/heterophilous-graphs) (MIT).
Conversion is scripted and deterministic; label counts above are computed from the
files in this repository, not copied from upstream docs.

## Citation

Paper: https://arxiv.org/abs/2302.11640

```bibtex
@inproceedings{platonov2023critical,
  title={A critical look at the evaluation of {GNNs} under heterophily: Are we really making progress?},
  author={Platonov, Oleg and Kuznedelev, Denis and Diskin, Michael and Babenko, Artem and Prokhorenkova, Liudmila},
  booktitle={International Conference on Learning Representations},
  year={2023}
}
```