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..
Wasserstein Embedding for Graph Learning
Authors: Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our new graph embedding approach on various benchmark graph-property prediction tasks, showing state-of-the-art classification performance while having superior computational efficiency. |
| Researcher Affiliation | Collaboration | HRL Laboratories, LLC., University of Virginia |
| Pseudocode | No | The paper describes methods and numerical details in text and equations but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | The code is available at https://github.com/navid-naderi/WEGL. |
| Open Datasets | Yes | We first evaluate our algorithm on the molecular property prediction task on the ogbg-molhiv dataset. This dataset is part of the Open Graph Benchmark (Hu et al., 2020)... We also consider a set of social network, bioinformatics and molecule graph datasets (Kersting et al., 2020). |
| Dataset Splits | Yes | To train and evaluate our proposed method, we use the scaffold split provided by the dataset... we perform 10-fold cross-validation with random splitting on the entire dataset |
| Hardware Specification | Yes | We carry out our experiments for WEGL and WWL on a 2.3 GHz Intel R Xeon R E5-2670 v3 CPU, while we use a 16 GB NVIDIA R Tesla R P100 GPU for GIN. |
| Software Dependencies | Yes | Auto-ML: Auto-Sklearn 2.0: The next generation. ar Xiv preprint ar Xiv:2007.04074, 2020. |
| Experiment Setup | Yes | We perform a grid search over a set of hyperparameters and report the configuration that leads to the best validation performance... HYPERPARAMETERS We use the following set of hyperparameters to perform a grid search over in each of the experiments: Random Forest: min_samples_leaf {1, 2, 5}, min_samples_split {2, 5, 10}, and n_estimators {25, 50, 100, 150, 200}. Gradient Boosted Decision Tree (GBDT): min_samples_leaf {1, 2, 5}, min_samples_split {2, 5, 10}, n_estimators {25, 50, 100, 150, 200}, and max_depth {1, 3, 5}. SVM-Linear and SVM-RBF: C {10 2, . . . , 105}. Multi-Layer Perceptron (MLP): hidden_layer_sizes {(128), (256), (128, 64), (256, 128)}. Auto-ML: Auto-Sklearn 2.0 searches over a space of 42 hyperparameters using Bayesian optimization techniques, as mentioned in Feurer et al. (2020). Number of Diffusion Layers in equation 7 and equation 14: L {3, . . . , 8}. Initial Node Feature Dimensionality (for ogbg-molhiv only): {100, 300, 500}. |