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..
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Authors: Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our proposed DGC improves linear GCNs by a large margin and makes them competitive with many modern variants of non-linear GCNs. (from Abstract) and In this section, we conduct a comprehensive analysis on DGC and compare it against both linear and non-linear GCN variants on a collection of benchmark datasets. |
| Researcher Affiliation | Academia | 1 School of Mathematical Sciences, Peking University, Beijing, China 2 Key Lab. of Machine Perception, School of Artificial Intelligence, Peking University, Beijing, China 3 Institute for Artificial Intelligence, Peking University, Beijing, China 4 Pazhou Lab, Guangzhou, China |
| Pseudocode | No | The paper presents equations for its proposed methods (DGC-Euler, DGC-RK) and summarizes propagation rules in Table 1, but does not include a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/yifeiwang77/DGC. |
| Open Datasets | Yes | For semi-supervised node classification, we use three standard citation networks, Cora, Citeseer, and Pubmed [18] and Reddit networks [5]. |
| Dataset Splits | Yes | For fully-supervised node classification, we randomly split the nodes into 60%, 20%, 20% for training, validation and testing. For semi-supervised node classification, we use the standard split, i.e., 20 labels per class for training, 500 labels for validation and 1000 labels for testing. |
| Hardware Specification | Yes | Table 5: Comparison of explicit computation time of different training stages on the Pubmed dataset with a single NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer [9]' but does not provide specific software dependencies with version numbers for the overall experimental setup. |
| Experiment Setup | Yes | For DGC, we use Adam optimizer [9] with learning rate 0.01, and the training epoch is 200 for semi-supervised tasks and 500 for fully-supervised tasks. The optimal T for DGC is chosen from {0.1, 0.2, . . . , 10} on the validation set. And K is chosen from {2, 5, 10, 20, 50, 100}. |