Deep Ensembles for Graphs with Higher-order Dependencies
Authors: Steven Krieg, William Burgis, Patrick Soga, Nitesh Chawla
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate DGE against eight state-of-the-art baselines on six real-world data sets with known higher-order dependencies, and show that, even with similar parameter budgets, DGE consistently outperforms baselines on semisupervised (node classification) and supervised (link prediction) tasks. |
| Researcher Affiliation | Academia | Steven J. Krieg, William C. Burgis, Patrick M. Soga, & Nitesh V. Chawla Lucy Family Institute for Data and Society University of Notre Dame Notre Dame, IN 46556 {skrieg,wburgis,psoga,nchawla}@nd.edu |
| Pseudocode | No | The paper describes its methods textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and 3 data sets are available at https://github.com/sjkrieg/dge. |
| Open Datasets | Yes | Code and 3 data sets are available at https://github.com/sjkrieg/dge. ... clickstreams of users playing the Wikispeedia game (Wiki) (West et al., 2009) |
| Dataset Splits | Yes | Node classification results (mean micro F1 for 5-fold cross validation) under various parameter budgets. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU or CPU models, or memory used for experiments. |
| Software Dependencies | Yes | We used Python 3.7.3 and Tensorfow 2.4.1 for all experiments, and utilized Stellargraph 1.2.1 (Data61, 2018) for the implementation of DGE. |
| Experiment Setup | Yes | For DGE, unless noted otherwise, we fixed ℓ= 16 and used the mean-pooling variant of Graph SAGE as the base GNN... We manually tuned each model (details in Appendix C). |