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..
Generative Causal Explanations for Graph Neural Networks
Authors: Wanyu Lin, Hao Lan, Baochun Li
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real-world datasets show that Gem achieves a relative increase of the explanation accuracy by up to 30% and speeds up the explanation process by up to 110 as compared to its state-of-the-art alternatives. |
| Researcher Affiliation | Academia | 1Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China 2Department of Electrical & Computer Engineering, University of Toronto, Toronto, Canada. |
| Pseudocode | Yes | Algorithm 1 Distillation Process: Distill the top-k most relevant edges for each computation graph |
| Open Source Code | Yes | The source code can be found in https://github.com/wanyulin/ICML2021-Gem. |
| Open Datasets | Yes | For graph classification, we use two benchmark datasets from bioinformatics Mutag (Debnath et al., 1991) and NCI1 (Wale et al., 2008). |
| Dataset Splits | Yes | Table 4 shows the detailed data splitting for model training, testing, and validation. Note that both classification models and our explanation models use the same data splitting. |
| Hardware Specification | Yes | All the experiments were performed on a NVIDIA GTX 1080 Ti GPU with an Intel Core i7-8700K processor. |
| Software Dependencies | No | Unless otherwise stated, all models, including GNN classification models and our explainer, are implemented using Py Torch 1 and trained with Adam optimizer. |
| Experiment Setup | Yes | Specifically, we first apply an inference model parameterized by a three-layer GCN with output dimensions 32, 32, and 16. Then the generative model is given by an inner product decoder between latent variables. The explainer models are trained with a learning rate of 0.01. We use mean square error as the loss for training Gem. In particular, it was optimized using Adam optimizer with a learning rate of 0.01 and 0.001 for explaining graph and node classification model, respectively. We train at batch size 32 for 100 epochs. |