Wasserstein Embedding for Graph Learning
Authors: Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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}. |