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..
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Authors: Yuhan Chen, Yihong Luo, Yifan Song, Pengwen Dai, Jing Tang, Xiaochun Cao
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate that De GEM, without OOD exposure during training, surpasses previous state-of-the-art methods, achieving an average AUROC improvement of 6.71% on homophilic graphs and 20.29% on heterophilic graphs, and even outperform methods trained with OOD exposure. Our code is available at: https://github.com/draym28/De GEM. |
| Researcher Affiliation | Academia | Yuhan Chen1 & Yihong Luo2 , Yifan Song3, Pengwen Dai4, Jing Tang3,2 , Xiaochun Cao4 1 The School of Computer Science and Engineering, Sun Yat-sen University 2 The Hong Kong University of Science and Technology 3 The Hong Kong University of Science and Technology (Guangzhou) 4 The School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University |
| Pseudocode | Yes | Algorithm 1 Training algorithm of De GEM. |
| Open Source Code | Yes | Our code is available at: https://github.com/draym28/De GEM. |
| Open Datasets | Yes | We evaluate De GEM on seven benchmark datasets for node classification tasks (Yang et al., 2016; Shchur et al., 2018; Rozemberczki et al., 2021; Wang et al., 2020; Pei et al., 2020), including four homophily datasets (Cora, Amazon-Photo, Twitch, and ogbn-Arxiv) and three heterophily datasets (Chameleon, Actor, and Cornell). |
| Dataset Splits | Yes | We split the ID dataset as 10%/10%/80% (train/valid/test), and use all the nodes in OOD dataset for evaluation. |
| Hardware Specification | Yes | We implement our model by Py Torch and conduct experiments on 24GB RTX-3090ti. |
| Software Dependencies | No | We implement our model by Py Torch and conduct experiments on 24GB RTX-3090ti. (No specific version number for PyTorch or other libraries is provided in the main text.) |
| Experiment Setup | Yes | Epoch number E = 200, MH layer number L = 5, hidden dimension d = 512, MCMC steps K = 20. We use Optuna (Akiba et al., 2019) to search hyper-parameters for our proposed model and baselines (see Appendix E.3 for detailed search space). |