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..
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Authors: Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments for both node classification and graph classification to show that MGNNI outperforms representative baselines and has a better ability for multiscale modeling and capturing of long-range dependencies. |
| Researcher Affiliation | Academia | Juncheng Liu Bryan Hooi Kenji Kawaguchi Xiaokui Xiao National University of Singapore EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual explanations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation can be found at https://github.com/liu-jc/MGNNI |
| Open Datasets | Yes | We use the standard train/test/val splits as in Pei et al. [23]. See the detailed setting in Appendix C.2. ... The same train/val/test split are used as in Hamilton et al. [14]. |
| Dataset Splits | Yes | We use the standard train/test/val splits as in Pei et al. [23]. ... 10-fold Cross-validation is conducted as [31] |
| Hardware Specification | No | The main body of the paper does not specify the hardware used (e.g., GPU models, CPU types, or cloud instances). While Appendix C is mentioned for resource details in the checklist, it is not provided in the given text. |
| Software Dependencies | No | The paper mentions 'Py Torch [22]' without a specific version number and does not list other software dependencies with their versions. |
| Experiment Setup | No | The main paper refers to Appendix C for detailed experimental settings ('See the detailed setting in Appendix C.2.'), indicating that specific hyperparameters and training configurations are not included in the provided text. |