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..
Explaining Graph Neural Networks via Structure-aware Interaction Index
Authors: Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various graph datasets and models demonstrate that our method consistently provides superior subgraph explanations compared to state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Yale University 2Vin AI Research 3The Chinese University of Hong Kong. |
| Pseudocode | Yes | Algorithm 1 Permutation-based sampling algorithm for the k-order Myerson-Taylor index. Algorithm 2 Value of an interaction-restricted function (f|E(T)). |
| Open Source Code | Yes | Our implementation is available at: https://github. com/ngocbh/MAGE/ |
| Open Datasets | Yes | We use ten datasets commonly used in the graph explainability literature, including synthetic data, biological, text, and image data. For synthetic datasets, we use Ba-2Motifs (Luo et al., 2020), BA-House Grid (Amara et al., 2023), and SPMotif (Wu et al., 2022) for classification tasks |
| Dataset Splits | Yes | We split the dataset into training, validation, and test subsets with respective ratios of 0.8, 0.1, and 0.1. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | Yes | One strategy to solve (3) is by linear relaxations and then using off-the-shelf MILP solvers such as MOSEK (Ap S, 2019) or GUROBI (Gurobi Optimization, LLC, 2023). |
| Experiment Setup | Yes | Regarding hyperparameter settings, we set the number of explanatory nodes M and components m according to the ground truth explanations for all the baselines if they are available. ... The number of permutations used to compute the Myerson-Taylor index is set to 200, and we use MOSEK (Ap S, 2019) with default parameters for the motif search. |