Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
Authors: Siqi Miao, Mia Liu, Pan Li
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eight datasets show that GSAT outperforms the state-of-the-art methods by up to 20% in interpretation AUC and 5% in prediction accuracy. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, West Lafayette, USA 2Department of Physics and Astronomy, Purdue University, West Lafayette, USA. Correspondence to: Siqi Miao <EMAIL>, Pan Li <EMAIL>. |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | Yes | Our code is available at https: //github.com/Graph-COM/GSAT. |
| Open Datasets | Yes | Mutag (Debnath et al., 1991) is a molecular property prediction dataset... OGBG-Molhiv (Wu et al., 2018; Hu et al., 2020) is a molecular property prediction datasets... |
| Dataset Splits | Yes | For Ba-2Motifs, we split it randomly into three sets (80%/10%/10%). For Mutag, we split it randomly into 80%/20% to train and validate models... For MNIST-75sp, we use the default splits given by (Knyazev et al., 2019)... For Graph-SST2, Spurious-Motifs and OGBG-Mol, we use the default splits given by (Yuan et al., 2020b) and (Wu et al., 2022). |
| Hardware Specification | Yes | IB-subgraph needs 40 hours to train 100 epochs for 1 seed on Spurious-Motif and 150 hours for OGBG-Molhiv on a Quadro RTX 6000. |
| Software Dependencies | No | The paper mentions software components like GIN, PNA, and Gumbel-softmax but does not provide specific version numbers for any of them, nor for programming languages or other libraries. |
| Experiment Setup | Yes | We use a two-layer GIN (Xu et al., 2019) with 64 hidden dimensions and 0.3 dropout ratio. We use the setting from (Corso et al., 2020) for PNA, which has 4 layers with 80 hidden dimensions, 0.3 dropout ratio, and no scalars are used... All datasets use a batch size of 128; except for MNIST-75sp we use a batch size of 256... GIN uses 0.003 learning rate for Spurious-Motifs and 0.001 for all other datasets... Ba-2Motif and Mutag use r = 0.5, and all other datasets use r = 0.7... β is not tuned and is set to 1 |E| for all datasets... Temperature used in the Gumbel-softmax trick (Jang et al., 2017) is not tuned, and we use 1 for all datasets. |