Space-Time Graph Neural Networks
Authors: Samar Hadou, Charilaos I Kanatsoulis, Alejandro Ribeiro
ICLR 2022 | Venue PDF | Archive PDF | Plain Text | 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 |