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..
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Authors: Shengyao Lu, Bang Liu, Keith G Mills, Jiao He, Di Niu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on a total of seven datasets, demonstrating its superior performance and efficiency both quantitatively and qualitatively over the leading baselines. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Alberta 2DIRO, Universit e de Montr eal & Mila, Canada CIFAR AI Chair 3Kirin AI Algorithm & Solution, Huawei. |
| Pseudocode | Yes | Algorithm 1 Linear-Complexity Search for Subgraph |
| Open Source Code | Yes | Our code is available at: https://github.com/sluxsr/Ei G-Search. |
| Open Datasets | Yes | We conduct experiments both on the synthetic dataset BA-2Motifs (Luo et al., 2020), and the real-world datasets MUTAG (Debnath et al., 1991), Mutagenicity (Kazius et al., 2005), NCI1 (Wale & Karypis, 2006). |
| Dataset Splits | Yes | The GNNs are trained with the following data splits: training set (80%), validation set (10%), testing set (10%). |
| Hardware Specification | Yes | All the experiments are conducted on Intel Core i7-10700 Processor and NVIDIA Ge Force RTX 3090 Graphics Card. |
| Software Dependencies | No | The paper mentions using GNN models like GCN and GIN, but does not specify any software libraries (e.g., PyTorch, TensorFlow) or their exact version numbers used in the experiments. |
| Experiment Setup | Yes | All the GNNs contain 3 message-passing layers and a 2-layer classifier, the hidden dimension is 32 for BA-2Motifs, BA-Shapes, and 64 for BA-Community, Tree-grid, MUTAG, Mutagenicity and NCI1. |