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---
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**.
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