| --- |
| 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**. |
|
|
|
|
