| ---
|
| pretty_name: GSCBench
|
| size_categories:
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| - 1M<n<10M
|
| tags:
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| - graph
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| - graph-edit-distance
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| - graph-matching
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| - benchmark
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| - cross-dataset-generalization
|
|
|
| ---
|
|
|
| # GSCBench
|
|
|
| GSCBench provides exact graph edit distance (GED) supervision for studying
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| in-collection, cross-collection, zero-shot and few-shot graph similarity models.
|
| The GSCBench benchmark data comprise **11 collections, 5,523 graphs and
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| 1,903,452 exact graph pairs**. The repository additionally includes a
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| version of PyG LINUX with isomorphic graphs removed (**89 graphs and 3,916
|
| pairs**), used as auxiliary data for model pretraining.
|
|
|
| ## Citation
|
|
|
| If you use GSCBench in your research, please cite our paper:
|
|
|
| **[Revisiting the Generalization of Neural Graph Edit Distance Models](https://arxiv.org/abs/2610.04644)**.
|
|
|
| ```bibtex
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| @article{liu2026revisiting,
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| title={Revisiting the Generalization of Neural Graph Edit Distance Models},
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| author={Liu, Zhouyang and Liu, Ning and Chen, Yixin and He, Jiezhong and Li, Dongsheng},
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| journal={arXiv preprint arXiv:2610.04644},
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| year={2026},
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| url={https://arxiv.org/abs/2610.04644}
|
| }
|
| ```
|
|
|
| ## Benchmark collections
|
|
|
| | Collection | Graphs | Exact pairs |
|
| | -------------- | -----: | ----------: |
|
| | tu_AIDS | 1,488 | 1,106,325 |
|
| | tu_BZR | 404 | 37,781 |
|
| | tu_COX2 | 465 | 78,100 |
|
| | tu_DHFR | 719 | 124,714 |
|
| | tu_PTC_MR | 328 | 52,706 |
|
| | tu_MUTAG | 175 | 15,220 |
|
| | tu_NCI1 | 992 | 379,032 |
|
| | tu_PROTEINS | 364 | 64,010 |
|
| | tu_ENZYMES | 117 | 6,286 |
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| | tu_IMDB-BINARY | 280 | 22,703 |
|
| | ogb_ogbg-code2 | 191 | 16,575 |
|
|
|
| The `tu_` prefix identifies TU collections, and `ogb_ogbg-code2` identifies
|
| the OGB code2 collection.
|
|
|
| ### Auxiliary pretraining data
|
|
|
| | Collection | Graphs | Pairs |
|
| | ------------- | -----: | ----: |
|
| | ged_pyg_LINUX | 89 | 3,916 |
|
|
|
| This PyG LINUX subset has isomorphic graphs removed and is provided to
|
| reproduce auxiliary model pretraining.
|
|
|
| ## Files and schema
|
|
|
| Each collection has four files:
|
|
|
| - `graphs.jsonl`: graph ID, node count, edge index, discrete node label IDs and
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| the `has_node_labels` flag. Edge indices contain both directions of each
|
| undirected edge.
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| - `ged_pairs.jsonl`: `graph_id_1`, `graph_id_2`, `ged`, `alignments`, and
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| `exact_elapsed_seconds`.
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| - `data.pt`: the same graph objects and ordered pair rows in a PyTorch dictionary
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| with keys `graphs_by_gid`, `rows`, `num_node_labels`, and `input_dim`.
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| Graph objects are PyTorch Geometric `Data` objects.
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| - `graph_id_map.csv`: released graph IDs and zero-based original collection IDs.
|
|
|
| Graph IDs are contiguous from zero within each collection. Pair endpoints refer
|
| to these released IDs. Node indices are preserved within each graph.
|
|
|
| `release_manifest.csv` and `release_manifest.json` list collection identities
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| and counts.
|
|
|
| ## GED and mappings
|
|
|
| `ged` is the unnormalized minimum edit cost between the two released graphs.
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| Graphs are undirected; each undirected edge is counted once even though
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| `edge_index` stores both directions.
|
|
|
| | Edit operation | Cost |
|
| | --------------------------------------------------------- | ---: |
|
| | Node insertion or deletion | 1 |
|
| | Substitution of different discrete node labels | 1 |
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| | Edge insertion or deletion | 1 |
|
|
|
| The cost model uses graph structure and discrete node labels. Deleting a node
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| also incurs the cost of deleting its incident edges.
|
|
|
| We retain one optimal mapping per graph pair in `alignments`. Each alignment
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| is a list of `[data_node, query_node]` pairs, from the data graph (`graph_id_1`)
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| to the query graph (`graph_id_2`); `-1` denotes a deletion or insertion. For example, `[2, 5]` matches data node 2 to query node 5,
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| `[3, -1]` deletes data node 3, and `[-1, 4]` inserts query node 4.
|
|
|
| `exact_elapsed_seconds` records the exact computation time in seconds.
|
|
|
| ## Download and read
|
|
|
| Install the download client in your Python environment:
|
|
|
| ```bash
|
| pip install huggingface_hub
|
| ```
|
|
|
| Download the 11 benchmark collections and auxiliary LINUX data into a relative local directory:
|
|
|
| ```python
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| from huggingface_hub import snapshot_download
|
|
|
| root = snapshot_download(
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| repo_id="ush3r/GSCbench",
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| repo_type="dataset",
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| local_dir="GSCbench",
|
| )
|
| ```
|
|
|
| To download and read just MUTAG (with PyTorch and PyG installed):
|
|
|
| ```python
|
| from huggingface_hub import snapshot_download
|
| import torch
|
|
|
| root = snapshot_download(
|
| repo_id="ush3r/GSCbench",
|
| repo_type="dataset",
|
| local_dir="GSCbench",
|
| allow_patterns=["tu_MUTAG/*"],
|
| )
|
| bundle = torch.load(root + "/tu_MUTAG/data.pt",
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| map_location="cpu", weights_only=False)
|
| row = bundle["rows"][0]
|
| data_graph = bundle["graphs_by_gid"][row["graph_id_1"]]
|
| query_graph = bundle["graphs_by_gid"][row["graph_id_2"]]
|
| ```
|
|
|
| Reading `data.pt` requires PyTorch and PyTorch Geometric. The files have been
|
| checked with PyTorch 2.5.1 and PyG 2.6.1. The two JSONL files can be read with
|
| Python's standard `json` module. The download includes all graphs and pair
|
| labels needed for training.
|
|
|
| To read the portable JSONL representation from a local download:
|
|
|
| ```python
|
| import json
|
| from pathlib import Path
|
|
|
| folder = Path("GSCbench/tu_MUTAG")
|
| with (folder / "graphs.jsonl").open(encoding="utf-8") as handle:
|
| graphs = {record["graph_id"]: record for record in map(json.loads, handle)}
|
| with (folder / "ged_pairs.jsonl").open(encoding="utf-8") as handle:
|
| row = json.loads(next(handle))
|
| data_graph = graphs[row["graph_id_1"]]
|
| query_graph = graphs[row["graph_id_2"]]
|
| ```
|
|
|
| The benchmark's existing `GSCDataset` loader reads `data.pt` when present.
|
| Otherwise it builds and saves that file from `graphs.jsonl` and
|
| `ged_pairs.jsonl`. Feature encoding and splits are applied when loading.
|
|
|
| ## Use with the benchmark code
|
|
|
| After installing the GSCBench code and its dependencies using the code
|
| repository's installation instructions, load a downloaded collection with
|
| its existing loader:
|
|
|
| ```python
|
| from gscbench.data.gscdataset import GSCDataset
|
|
|
| dataset = GSCDataset(
|
| dataset_name="tu_MUTAG",
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| root_dir="GSCbench",
|
| split_seed=1729,
|
| val_ratio=0.2,
|
| test_ratio=0.2,
|
| node_label_encoding="fixed_one_hot",
|
| node_label_dim=97,
|
| )
|
| dataset.load()
|
| train = dataset.get_split("train")
|
| val = dataset.get_split("val")
|
| test = dataset.get_split("test")
|
| pair = train.samples[0]
|
| ```
|
|
|
| These values illustrate loading; use the experiment config's seed, ratios,
|
| feature encoding and pair budget for paper experiments. Set the config's
|
| `gscbench_root_dir` to the downloaded dataset directory.
|
|
|
| ## Experimental splits
|
|
|
| The release does not impose one train/validation/test partition. Benchmark
|
| configs select the graph split seed, validation/test ratios, feature encoding
|
| and training pair budget. The default benchmark graph split uses 60% training,
|
| 20% validation and 20% test graphs (with integer rounding). Training uses
|
| train-train pairs; validation uses val-train pairs; the main test view contains
|
| test-train, test-val and test-test pairs. Zero-shot evaluation does not train on
|
| the target collection. Few-shot fitting uses only the selected target training
|
| pairs.
|
|
|
| Validation-validation pairs remain in the released data but are not used by
|
| the default validation view. A training pair budget caps the number of
|
| train-train pairs, rather than training graphs. The graph partition stays
|
| the same across pair budgets for a given split seed.
|
|
|
| ## Sources
|
|
|
| The graphs originate from the following collections:
|
|
|
| - The ten `tu_` collections come from
|
| [TUDataset](https://chrsmrrs.github.io/datasets/). The collection names in
|
| the table identify their entries in the
|
| [upstream dataset list](https://chrsmrrs.github.io/datasets/docs/datasets/).
|
| - `ogb_ogbg-code2` comes from
|
| [OGB's ogbg-code2 collection](https://ogb.stanford.edu/docs/graphprop/).
|
| - `ged_pyg_LINUX` uses graphs from the LINUX collection distributed through
|
| [PyG GEDDataset](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.datasets.GEDDataset.html).
|
|
|
| `graph_id_map.csv` connects each released graph to its zero-based index in
|
| the original source collection.
|
|
|
| ## Reproducibility
|
|
|
| For a reproducible experiment, record the Hugging Face repository commit
|
| and pass it as `revision` to `snapshot_download`, together with the benchmark
|
| code revision, experiment config and split seed. The paper and code should
|
| refer to that same data revision. |
