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..
On Explaining Equivariant Graph Networks via Improved Relevance Propagation
Authors: Hongyi Ling, Haiyang Yu, Zhimeng Jiang, Na Zou, Shuiwang Ji
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on both synthetic and real-world datasets, our method demonstrates its capability to identify critical geometric structures and outperform alternative baselines. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Texas A&M University, Texas, USA 2Department of Industrial Engineering, University of Houston, Texas, USA. |
| Pseudocode | No | The paper describes the methodology using mathematical formulations and descriptive text, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code has been released as part of the AIRS library (https://github.com/divelab/AIRS/). |
| Open Datasets | Yes | In addition to synthetic datasets containing perfect 3D geometric shapes, we evaluate our method on three real-world datasets, including the Structural Classification of Proteins (SCOP), Bio Li P, and Actstrack. The SCOP database (Murzin et al., 1995; Andreeva et al., 2007; Chandonia et al., 2019) is a predominantly manually curated classification of protein structural domains... Bio Li P (Yang et al., 2012; Zhang et al., 2024) is a semi-manually curated database dedicated to high-quality ligand-protein interactions... Acts Track (Miao et al., 2023) is a particle tracking simulation dataset in high-energy physics. |
| Dataset Splits | No | The paper mentions using 'training and validation datasets' for SCOP by referencing external papers (Hou et al., 2018; Hermosilla et al., 2020), and mentions a 'test dataset' for Bio Li P, but does not provide explicit percentages, sample counts, or detailed splitting methodologies for all datasets, particularly the synthetic Shapes and Spiral Noise datasets. |
| Hardware Specification | Yes | We use NVIDIA RTX A6000 GPUs for all our experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Table 4 provides details on the number of layers, the number of hidden equivariant features, and the highest order of equivariant feature lmax in the TFN. |