Graph Neural Networks Exploration
Prieskum techník grafových neurónových sietí
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České vysoké učení technické v Praze
Czech Technical University in Prague
Czech Technical University in Prague
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Táto práca sa venuje rozboru metód grafových neurónových sietí pre klasifikáciu vrcholov a grafov. Skúma súčasné knižnice na prácu s grafovými neurónovými sieťami ako StellarGraph, PyTorch Geometric a DGL. Na vybraných datasetoch z Open Graph Benchmark sú otestované a porovnané grafové algoritmy Graph Convolutional Networks, GraphSAGE a Graph Attention Networks. Dosiahnuté výsledky sú porovnané so state of the art výsledkami.
This thesis is dedicated to the analysis of graph neural network methods for the nodes and graph classification. Explores current libraries for working with graph neural networks such as StellarGraph, PyTorch Geometric and DGL. The graph algorithms Graph Convolutional Networks, GraphSAGE and Graph Attention Networks are tested and compared on selected datasets from the Open Graph Benchmark. The achieved results are compared with the state of the art results.
This thesis is dedicated to the analysis of graph neural network methods for the nodes and graph classification. Explores current libraries for working with graph neural networks such as StellarGraph, PyTorch Geometric and DGL. The graph algorithms Graph Convolutional Networks, GraphSAGE and Graph Attention Networks are tested and compared on selected datasets from the Open Graph Benchmark. The achieved results are compared with the state of the art results.