AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
Authors: Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, Yibing Zhan, Yiheng Lu, Dapeng Tao
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across seven datasets on semi-supervised node classification benchmarks demonstrate AGMixup s superiority over state-of-the-art graph mixup methods. The semi-supervised node classification accuracy results presented in Table 1 are obtained from ten different runs, ensuring reliable and consistent measurements. We provide comprehensive empirical analysis to understand the behavior of AGMixup. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Xidian University, Xi an, China 2JD Explore Academy, Xidian University, Beijing, China 3School of Information Science and Engineering, Yunnan University, Kunming, China {wglu@.stu., zyguan@, ywzhao@mail., yym@,}xidan.edu.cn, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology, including subgraph-centric mixup, contextual similarity-aware λ initialization, and uncertainty-aware λ adjustment using equations, but it does not present these steps in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | We compare our AGMixup2 with three state-of-the-art graph mixup methods targeting at the semi-supervised node classification problem... 2https://github.com/Weigang Lu/AGMixup |
| Open Datasets | Yes | As for the datasets, we choose seven graphs, i.e., Cora, Citeseer, Pubmed (Yang, Cohen, and Salakhudinov 2016), Coauthor CS, and Coauthor Physics (Shchur et al. 2018), and two large-scale graphs, i.e., ogbn-arxiv and ogbn-products (Hu et al. 2020). |
| Dataset Splits | No | The paper mentions using common datasets for semi-supervised node classification, but it does not explicitly state the specific train/test/validation split percentages, sample counts, or the exact splitting methodology used for these datasets within the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation of AGMixup or the experiments. |
| Experiment Setup | Yes | In this section, we explore the impact of key hyperparameters on the performance of AGMixup, i.e., subgraph size (controlled by r) and sensitivity to similarity and uncertainty (controlled by γ and β, respectively). We conduct experiments on Cora and Pubmed using GCN as the backbone model. For the Cora dataset, AGMixup exhibits optimal performance at r = 3... For the Pubmed dataset, AGMixup shows improved performance as r is increased up to a moderate level (r = 5)... we recommend setting γ and β at the range of 0.5 to 2. |