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@@ -56,7 +56,7 @@ PolyGraph discrepancy is a new metric we introduced, which provides the followin
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It also provides a number of other advantages over MMD which we discuss in our paper.
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It also provides a number of other advantages over MMD which we discuss in [our paper](https://arxiv.org/abs/2510.06122).
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## Installation
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-`SBMGraphDataset`
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-`LobsterGraphDataset`
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But they should not be used for benchmarking, due to unreliable metric estimates (see our paper for more details).
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But they should not be used for benchmarking, due to unreliable metric estimates (see [our paper](https://arxiv.org/abs/2510.06122) for more details).
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We provide larger datasets that should be used instead:
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-`PlanarLGraphDataset`
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```
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## Example Benchmark
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The following results mirror the tables from our paper. Bold indicates best, and underlined indicates second-best. Values are multiplied by 100 for legibility. Standard deviations are obtained with subsampling using `StandardPGDInterval` and `MoleculePGDInterval`. Specific parameters are discussed in the paper.
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The following results mirror the tables from [our paper](https://arxiv.org/abs/2510.06122). Bold indicates best, and underlined indicates second-best. Values are multiplied by 100 for legibility. Standard deviations are obtained with subsampling using `StandardPGDInterval` and `MoleculePGDInterval`. Specific parameters are discussed in [the paper](https://arxiv.org/abs/2510.06122).
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<divalign="center">
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<table>
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<sub>* AutoGraph* denotes a variant that leverages additional training heuristics as described in the paper.</sub>
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<sub>* AutoGraph* denotes a variant that leverages additional training heuristics as described in the [paper](https://arxiv.org/abs/2510.06122).</sub>
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## Citing
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To cite our paper:
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```latex
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@misc{krimmel2025polygraph,
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title={PolyGraph Discrepancy: a classifier-based metric for graph generation},
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author={Markus Krimmel and Philip Hartout and Karsten Borgwardt and Dexiong Chen},
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