---
pretty_name: "IsoNet++ Benchmark"
tags:
  - graphs
  - graph-retrieval
  - subgraph-isomorphism
  - graph-mining
  - graph-datasets
task_categories:
  - graph-ml
  - other
license: "cc-by-4.0"
---

# IsoNet++ Benchmark Dataset

The **IsoNet++ Benchmark** is a  *subgraph retrieval* benchmark derived from TUDataset graph datasets including:

- **AIDS**
- **MUTAG**
- **PTC** (FM, FR, MM, MR)

The benchmark is used to evaluate models that learn **graph representations** for:
- Graph similarity search
- Subgraph matching
- Retrieval at scale

This benchmark was introduced to evaluate the **IsoNet++** model.

---

## Dataset Structure

```
isonetpp-benchmark/
├─ corpus/                      # Searchable  graph collections
│   ├─ aids240k_corpus_subgraphs.pkl
│   ├─ mutag240k_corpus_subgraphs.pkl
│   ├─ ptc_fm240k_corpus_subgraphs.pkl
│   ├─ ptc_fr240k_corpus_subgraphs.pkl
│   ├─ ptc_mm240k_corpus_subgraphs.pkl
│   └─ ptc_mr240k_corpus_subgraphs.pkl
└─ splits/                      # Query → relevance evaluation sets
    ├─ train/
    │   ├─ train_<dataset>_query_subgraphs.pkl
    │   └─ train_<dataset>_rel_nx_is_subgraph_iso.pkl
    ├─ val/
    │   ├─ val_<dataset>_query_subgraphs.pkl
    │   └─ val_<dataset>_rel_nx_is_subgraph_iso.pkl
    └─ test/
        ├─ test_<dataset>_query_subgraphs.pkl
        └─ test_<dataset>_rel_nx_is_subgraph_iso.pkl
```

Where `<dataset>` ∈ `{aids240k, mutag240k, ptc_fm240k, ptc_fr240k, ptc_mm240k, ptc_mr240k}`.

---

## Data Format

All `.pkl` files use Python `pickle` serialization:

| File Pattern | Description |
|-------------|-------------|
| `*_corpus_subgraphs.pkl` | List of NetworkX graphs representing the retrieval corpus |
| `*_query_subgraphs.pkl` | List of NetworkX graphs serving as query graphs |
| `*_rel_nx_is_subgraph_iso.pkl` | Binary labels from exact subgraph isomorphism (NetworkX VF2) |

---

## Load Examples

### Load Corpus

```python
from huggingface_hub import hf_hub_download
import pickle

path = hf_hub_download(
    "structlearning/isonetpp-benchmark",
    filename="large_dataset/corpus/aids240k_corpus_subgraphs.pkl",
    repo_type="dataset"
)
with open(path, "rb") as f:
    corpus_graphs = pickle.load(f)
```

### Load Query Split

```python
from huggingface_hub import hf_hub_download
import pickle

queries = pickle.load(open(
    hf_hub_download("structlearning/isonetpp-benchmark",
                    filename="large_dataset/splits/train/train_aids240k_query_subgraphs.pkl",
                    repo_type="dataset"),
    "rb"
))

labels = pickle.load(open(
    hf_hub_download("structlearning/isonetpp-benchmark",
                    filename="large_dataset/splits/train/train_aids240k_rel_nx_is_subgraph_iso.pkl",
                    repo_type="dataset"),
    "rb"
))
```

---

## Intended Use

This dataset is suitable for:

- Graph retrieval model evaluation
- Learning subgraph-aware representations
- Benchmarking hashing, GNN-based retrieval systems
- Reproducing IsoNet++ results

---

## Citation

If you use this dataset in research, please cite:

```
@inproceedings{ramachandraniteratively,
  title={Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval},
  author={Ramachandran, Ashwin and Raj, Vaibhav and Roy, Indradyumna and Chakrabarti, Soumen and De, Abir},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
}
```

---

## License

This dataset is released under **CC-BY-4.0**.