Graph U-Nets
Authors: Hongyang Gao, Shuiwang Ji
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on node classification and graph classification tasks demonstrate that our methods achieve consistently better performance than previous models. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Texas A&M University, TX, USA. Correspondence to: Hongyang Gao <hongyang.gao@tamu.edu>, Shuiwang Ji <sji@tamu.edu>. |
| Pseudocode | No | The paper describes the proposed operations and architecture through text and mathematical equations, but it does not include a separate pseudocode block or an explicitly labeled algorithm figure. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We employ three benchmark datasets for this setting; those are Cora, Citeseer, and Pubmed (Kipf & Welling, 2017), which are summarized in Table 1. We use protein datasets including D&D (Dobson & Doig, 2003) and PROTEINS (Borgwardt et al., 2005), the scientific collaboration dataset COLLAB (Yanardag & Vishwanathan, 2015). These data are summarized in Table 2. |
| Dataset Splits | Yes | For each class, there are 20 nodes for training, 500 nodes for validation, and 1000 nodes for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow, or CUDA version) used for the experiments. |
| Experiment Setup | Yes | For transductive learning tasks, we employ our proposed g-U-Nets proposed in Section 3.3. ... We sample 2000, 1000, 500, 200 nodes in the four g Pool layers, respectively. ... we apply L2 regularization on weights with λ = 0.001. Dropout (Srivastava et al., 2014) is applied to both adjacency matrices and feature matrices with keep rates of 0.8 and 0.08, respectively. ... We sample proportions of nodes in four g Pool layers; those are 90%, 70%, 60%, and 50%, respectively. The dropout keep rate imposed on feature matrices is 0.3. |