Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Graph Spectral Evolution Networks for Graph Topological Evolution
Authors: Negar Etemadyrad, Qingzhe Li, Liang Zhao7358-7366
AAAI 2021 | Venue PDF | 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. |