GitHub topics: dense-array
mahnoorsheikh16/Node-Classification-with-Graph-Neural-Networks
Evaluation of multiple graph neural network models—GCN, GAT, GraphSAGE, MPNN and DGI—for node classification on graph-structured data. Preprocessing includes feature normalization and adjacency-matrix regularization, and an ensemble of model predictions boosts performance. The best ensemble achieves 83.47% test accuracy.
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Related Keywords
deep-graph-infomax
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deep-infomax
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deep-learning
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deep-neural-networks
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dense-array
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ensemble-learning
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feature-normalization
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gcn-architecture
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graph-attention-network
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graph-neural-networks
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graphsage
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loss-functions
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mpnn-model
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node-classification
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pytorch-implementation
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pytorch-tensors
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regularization
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relu-layer
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softmax-function
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