Graph Ordering Attention Networks
Authors: Michail Chatzianastasis, Johannes Lutzeyer, George Dasoulas, Michalis Vazirgiannis
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform an extensive evaluation of our GOAT model and compare against a wide variety of state-of-the-art GNNs, on three synthetic datasets as well as on nine node-classification benchmarks. |
| Researcher Affiliation | Academia | Ecole Polytechnique, Institut Polytechnique de Paris, France 2 Harvard University, Cambridge, MA, USA |
| Pseudocode | No | The paper describes the architecture of the GOAT layer and its components using textual descriptions and a diagram, but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available on Git Hub1. 1Code: https://github.com/Michail Chatzianastasis/GOAT |
| Open Datasets | Yes | We utilize nine well-known node classification benchmarks to validate our proposed model in real-world scenarios originating from a variety of different applications. Specifically, we use 3 citation network benchmark datasets: Cora, Cite Seer (Sen et al. 2008), ogbn-arxiv (Hu et al. 2020), 1 disease spreading model: Disease (Chami et al. 2019), 1 social network: Last FM Asia (Rozemberczki and Sarkar 2020), 2 co-purchase graphs: Amazon Computers, Amazon Photo (Shchur et al. 2019) and 2 co-authorship graphs: Coauthor CS,Physics (Shchur et al. 2019). |
| Dataset Splits | Yes | We use 60/20/20 percent of nodes for training, validation and testing. We perform a hyperparameter search for all models on a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer but does not specify software dependencies like programming languages or libraries with version numbers. |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma and Ba 2015) with an initial learning rate of 0.005 and early stopping for all models and datasets. We perform a hyperparameter search for all models on a validation set. The hyperparameters include the size of hidden dimensions, dropout, and number of attention heads for GAT and GOAT. We fix the number of layers to 2. |