Bilinear Graph Neural Network with Neighbor Interactions
Authors: Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China 2National University of Singapore 3Beijing Kuaishou Technology Co., Ltd. Beijing, China 4University of Electronic Science and Technology of China |
| Pseudocode | No | The paper describes mathematical formulations and a model framework, but it does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codes are available at: https://github.com/zhuhm1996/bgnn. |
| Open Datasets | Yes | Following previous works [Sen et al., 2008; Yang et al., 2016; Veliˇckovi c et al., 2018], we utilize three benchmark datasets of citation network Pubmed, Cora and Citeseer [Sen et al., 2008]. |
| Dataset Splits | Yes | That is, 20 labeled nodes per class are used for training. 500 nodes and 1000 nodes are used as validation set and test set, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, etc.) used in the experiments. |
| Experiment Setup | Yes | The dropout rates, λ, β and α are selected within [0, 0.2, 0.4, 0.6], [0, 1e-4, 5e-4, 1e-3], [0, 0.1, 0.3, , 0.9, 1] and [0, 0.1, 0.3, , 0.9, 1], respectively. All BGNN-based models are trained for 2,000 epochs with an early stopping strategy based on both convergence behavior and accuracy of the validation set. |