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..
Deep Ensembles for Graphs with Higher-order Dependencies
Authors: Steven Krieg, William Burgis, Patrick Soga, Nitesh Chawla
ICLR 2023 | Venue PDF | 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 EMAIL |
| 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). |