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 [1].
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
Authors: Chenxiao Yang, Qitian Wu, Junchi Yan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to validate the efficacy of our method on graph-structured data in terms of various types of privileged geometric knowledge, combinations of teacher-student GNN architectures and potential application scenarios. We use three benchmark datasets Cora [35], Citeseer [44], Pubmed [39], and a larger dataset OGBN-Arxiv [23] for node classification tasks. |
| Researcher Affiliation | Academia | Chenxiao Yang, Qitian Wu, Junchi Yan Department of Computer Science and Engineering Mo E Key Lab of Artificial Intelligence Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper describes algorithmic steps in prose but does not include a formally labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The codes are available at https://github.com/chr26195/GKD. |
| Open Datasets | Yes | We use three benchmark datasets Cora [35], Citeseer [44], Pubmed [39], and a larger dataset OGBN-Arxiv [23] for node classification tasks. |
| Dataset Splits | No | The paper mentions 'best validation accuracy' (Figure 4 caption) implying a validation set was used, and confirms 'data splits' were specified in the checklist (3b), but specific percentages or methods for creating these splits are not explicitly detailed in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, types of processors, or cloud instance specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The backbone f is set as 3-layer GCN [28] for both student and teacher models, unless otherwise stated. |