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..
Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
Authors: Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh Chawla
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and theoretical analyses demonstrate the superiority of NOSMOG by comparing it to GNNs and the state-of-the-art method in both transductive and inductive settings across seven datasets. |
| Researcher Affiliation | Academia | Yijun Tian1, Chuxu Zhang2, Zhichun Guo1, Xiangliang Zhang1, Nitesh V. Chawla1 1University of Notre Dame, 2Brandeis University |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/meettyj/NOSMOG. |
| Open Datasets | Yes | We use five widely used public benchmark datasets (i.e., Cora, Citeseer, Pubmed, A-computer, and A-photo) (Zhang et al., 2022b; Yang et al., 2021), and two large OGB datasets (i.e., Arxiv and Products) (Hu et al., 2020) to evaluate the proposed model. |
| Dataset Splits | Yes | We adopt accuracy to measure the model performance, use validation data to select the optimal model, and report the results on test data. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for experiments. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not explicitly provide details about the experimental setup such as hyperparameters or training settings in the main text. |