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..
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Authors: Snir Hordan, Tal Amir, Nadav Dym
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our claims via a separation experiment on combinatorial graphs that are 2-WL distinguishable, yet are 1-WL indistinguishable, via the EXP (Abboud et al., 2020) dataset. |
| Researcher Affiliation | Academia | 1Faculty of Mathematics, Technion Israel Institute of Technology, Haifa, Israel 2Faculty of Computer Science, Technion Israel Institute of Technology, Haifa, Israel. |
| Pseudocode | Yes | We define the Convolution Layer of We LNet , i.e. We LConv, which is based on EGNN, as c(i, j) = PPGNan(θ; , T)(X, V )i,j (10) mij = ϕe(hi, hj, eij, c(i, j)) (11) mi = j mi,j (12) xout i = xi + ϕn(mi)vi + X j ϕx(mi,j)(xj xi) + X j =i ϕv(mi,j)vj (13) vout i = ˆ ϕn(mi)vi + X j ˆϕx(mi,j)(xj xi) + X j =i ˆϕv(mi,j)vj (14) hout i = ϕh(hi, mi) (15) |
| Open Source Code | Yes | Code is available at https://www.github.com/ Intelli Finder/welnet |
| Open Datasets | Yes | We empirically evaluate our claims via a separation experiment on combinatorial graphs that are 2-WL distinguishable, yet are 1-WL indistinguishable, via the EXP (Abboud et al., 2020) dataset. |
| Dataset Splits | Yes | Following (Victor Garcia Satorras, 2021), we sampled 3,000 trajectories for training, 2,000 for validation and 2.000 for testing. |
| Hardware Specification | Yes | We ran our experiment on an NVIDIA A40 GPU with CUDA toolkit version 12.1. |
| Software Dependencies | Yes | We ran our experiment on an NVIDIA A40 GPU with CUDA toolkit version 12.1. |
| Experiment Setup | Yes | Table 4. Configuration of the We LNet Architecture. HYPERPARAMETER VALUE ACTIVATION SCALED SOFTPLUS EDGE FEATURES DIM 128 WL FEATURES DIM 32 LEARNING RATE 1E-3 OPTIMIZER ADAM SCHEDULER STEPLR NUMBER OF CONVOLUTIONS 4 2-WL ITERATIONS (T) 2 |