Implicit Graph Neural Networks
Authors: Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a comprehensive range of tasks show that IGNNs consistently capture long-range dependencies and outperform the state-of-the-art GNN models. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Sciences, University of California at Berkeley 2Tsinghua-Berkeley Shenzhen Institute, Tsinghua University 3Department of Computer Science and Technology, Tsinghua University |
| Pseudocode | No | The paper includes mathematical equations and descriptions of the model, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | Yes | Code available at https://github.com/Swiftie H/IGNN. |
| Open Datasets | Yes | To further manifest the scalability of IGNN towards larger graphs, we conduct experiments on a large multi-label node classification data set, namely the Amazon product co-purchasing network data set (Yang and Leskovec, 2015) 3. The data set renders products as nodes and co-purchases as edges but provides no input features. ... 3http://snap.stanford.edu/data/#amazon |
| Dataset Splits | Yes | We use a small training set, validation set, and test set with only 20, 100, and 200 nodes, respectively. ... The train/valid/test split is consistent with Graph Sage (Hamilton et al., 2017). ... 10-fold cross-validation with LIB-SVM (Chang and Lin, 2011) is conducted. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU models, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions 'LIB-SVM (Chang and Lin, 2011)' and alludes to 'autograd software', but it does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | Detailed description of the data sets, our preprocessing procedure, hyper-parameters, and other information of experiments can be found in Appendix E. |