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mahnoorsheikh16 / Node-Classification-with-Graph-Neural-Networks

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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.

deep-neural-networks deep-learning ensemble-learning regularization loss-functions relu-layer node-classification graphsage graph-neural-networks pytorch-implementation deep-infomax deep-graph-infomax gcn-architecture feature-normalization mpnn-model pytorch-tensors graph-attention-network softmax-function dense-array
  • Updated May 12, 2025
  • Jupyter Notebook

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