Deep Graph Spectral Evolution Networks for Graph Topological Evolution
Authors: Negar Etemadyrad, Qingzhe Li, Liang Zhao7358-7366
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple synthetic and real-world datasets demonstrate outstanding performance. |
| Researcher Affiliation | Academia | 1 George Mason University, Fairfax, VA, 22033 2 Emory University, Atlanta, GA, 30307 |
| Pseudocode | No | The paper includes a diagram (Figure 1) illustrating the network architecture, but no pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The proposed method is implemented with Pytorch deep learning framework.1 1https://github.com/netemady/GSEN |
| Open Datasets | Yes | Real-world HCP Dataset: ...obtained from the human connectome project (HCP) (Van Essen et al. 2013) 2. 2http://www.humanconnectomeproject.org/ |
| Dataset Splits | Yes | For all comparison and our methods, 5-fold cross-validation is performed, where for each run we select one subset as test and the remaining 4 as training set. In the training set, 20% is randomly selected as validation to determine hyperparameters through a grid search. |
| Hardware Specification | Yes | conducted on a 64-bit machine with 40 GB memory, a 4-core Intel CPU and an Nvidia RTX-2080 Ti GPU. |
| Software Dependencies | No | The proposed method is implemented with Pytorch deep learning framework. No specific version number for PyTorch is provided. |
| Experiment Setup | No | The parameter settings of GT-GAN, C-DGT, and our method are detailed in supplementary materials, where we also included key parameter sensitivity analyses. These details are not in the main text. |