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..
Space-Time Graph Neural Networks
Authors: Samar Hadou, Charilaos I Kanatsoulis, Alejandro Ribeiro
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments with decentralized control systems showcase the effectiveness and stability of the proposed ST-GNNs. Our theoretical findings are also supported by thorough experimental analysis based on decentralized control applications. |
| Researcher Affiliation | Academia | Department of Electrical and Systems Engineering University of Pennsylvania EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | We used the GNN library at https://github.com/alelab-upenn/graph-neural-networks |
| Open Datasets | No | The dataset is generated according to the mobility model in (95) and (96). The dataset consists of 500 time-varying graph signals {Xm}500 m=1 that are calculated under optimal centralized policies {U m}500 m=1. |
| Dataset Splits | Yes | We split the data into 460 examples for training, 20 for validation and 20 for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | We used the GNN library at https://github.com/alelab-upenn/graph-neural-networks |
| Experiment Setup | Yes | We train a 2-layer ST-GNN on the training data and optimize the mean squared loss using ADAM algorithm with learning rate 0.01 and decaying factors β1 = 0.9 and β2 = 0.999. Table 1: Simulation parameters in Experiments #1 and #2. parameter value... ST-GNN feature/layer, F0:2 4, 16, 2 (#1) and 6, 64, 2 (#2) Filter taps/layer, K1:2 4, 1 Activation function, σ tanh |