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..
Gradient Rewiring for Editable Graph Neural Network Training
Authors: Zhimeng Jiang, Zirui Liu, Xiaotian Han, Qizhang Feng, Hongye Jin, Qiaoyu Tan, Kaixiong Zhou, Na Zou, Xia Hu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of GRE on various model architectures and graph datasets in terms of multiple editing situations. |
| Researcher Affiliation | Academia | Zhimeng Jiang1, Zirui Liu2, Xiaotian Han3, Qizhang Feng1, Hongye Jin1, Qiaoyu Tan4, Kaixiong Zhou5, Na Zou6, Xia Hu7 1Texas A&M University, 2University of Minnesota, 3Case Western Reserve University, 4NYU Shanghai, 5North Carolina State University, 6University of Houston, 7Rice University |
| Pseudocode | Yes | Algorithm 1 Gradient Rewiring Editable (GRE) Graph Neural Networks Training Algorithm 2 Gradient Rewiring Editable Plus (GRE+) Graph Neural Networks Training |
| Open Source Code | Yes | The source code is available at https://github.com/zhimengj0326/Gradient_rewiring_editing. |
| Open Datasets | Yes | In our experiments, we utilize a selection of eight graph datasets from diverse domains, split evenly between small-scale and large-scale datasets. The small-scale datasets include Cora, A-computers [29], A-photo [29], and Coauthor-CS [29]. On the other hand, the large-scale datasets encompass Reddit [25], Flickr [2], ogbn-arxiv [3], and ogbn-products [3]. |
| Dataset Splits | Yes | Specifically, we first randomly split the train/validation/test dataset. Then, we ensure that each class has 20 samples in the training and 30 samples in the validation sets. The remaining samples are used for the test set. |
| Hardware Specification | Yes | For hardware configuration, all experiments are executed on a server with 251GB main memory, 24 AMD EPYC 7282 16-core processor CPUs, and a single NVIDIA Ge Force-RTX 3090 (24GB). |
| Software Dependencies | Yes | For software configuration, we use CUDA=11.3.1, python=3.8.0, pytorch=1.12.1, higher=0.2.1, torch-geometric=1.7.2, torch-sparse=0.6.16 in the software environment. |
| Experiment Setup | Yes | The hyperparameters for model architecture, learning rate, dropout rate, and training epochs are shown in Table 4. |