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 | Conference PDF | Archive PDF | Plain Text | 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. |