Towards a Data-Driven Graph Neural Network Model Selection
Graph Neural Networks (GNNs) have achieved great success in solving many machine learning tasks, and many different neural architectures have been proposed over the past few years. However, the lack of robust public benchmarks hinders the assessment of GNN architectures, which has made most research...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/411416 |
| Acceso en línea: | https://hdl.handle.net/2117/411416 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Machine learning Graph theory graph neural networks neural networks meta-learning machine learning deep learning artificial intelligence model selection neural architecture search graph theory graph model graph generator data science Xarxes neuronals (Informàtica) Aprenentatge automàtic Grafs, Teoria de Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Graph Neural Networks (GNNs) have achieved great success in solving many machine learning tasks, and many different neural architectures have been proposed over the past few years. However, the lack of robust public benchmarks hinders the assessment of GNN architectures, which has made most research papers rely on the same 5-10 datasets. Our first contribution is Graphlaxy++, a novel generative graph learning model designed to emulate GNN performance and that can be used to generate artificial benchmarks with user-defined metrics. Leveraging Graphlaxy++, we generate a diverse synthetic graph dataset that serves as input to a predictive statistical model that can predict the performance of GNNs. This model is able to successfully predict the relative performance of 4 different architectures: GCN, GAT, GIN, and MLP. The predictive capacity of this model offers potential applications in speeding up neural architecture searches and optimizing graph modeling strategies. |
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