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..
On Self-Distilling Graph Neural Network
Authors: Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Collaboration | Yuzhao Chen1,2, , Yatao Bian2, , Xi Xiao1,3, , Yu Rong2 , Tingyang Xu2 , Junzhou Huang2,4 1Tsinghua University 2Tencent AI Lab 3Peng Cheng Laboratory 4University of Texas at Arlington |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of source code. |
| Open Datasets | Yes | Node Classification Table 2 summarizes the results of GNNs with various depths on Cora, Citeseer and Pub Med [2008]. Graph Classification Table 3 summarizes the results of various popular GNNs on the graph kernel classification datasets, including ENZYMES, DD, and PROTEINS in TU dataset [2016]. |
| Dataset Splits | Yes | We follow the setting of semi-supervised learning, using the fixed 20 nodes per class for training. [...] each experiment is conducted by 10-fold cross validation with the splits ratio at 8:1:1 for training, validating and testing. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For our method, the hyper-parameters of α and β are both set to 0.01 and γ is 0. [...] The hyper-parameters of γ is fixed to 0 for node classification, and we determine α and β via a simple grid search. Details are provided in Appendix. [...] Hyper-parameter settings are deferred to Appendix. |