<ul><li>GraphSAGE is introduced as a solution to issues with GCNs and GATs, such as generalization and scalability problems.</li><li>GCNs and GATs struggle with generalizing to unseen graphs, requiring the same structure as training data.</li><li>GraphSAGE addresses scalability by sampling neighbors and aggregating features efficiently.</li><li>Sampling neighbors and aggregating their features are crucial steps in the GraphSAGE architecture.</li><li>Aggregation functions like mean aggregation, LSTM, and pooling are utilized in GraphSAGE.</li><li>Node representations are updated by combining previous features with aggregated neighbor features in GraphSAGE.</li><li>GraphSAGE allows information flow from distant neighbors through repeated layers in the network.</li><li>GraphSAGE is implemented in PyG, making it easily usable in PyTorch for predicting on graphs.</li><li>Results comparing GraphSAGE with GCN and GAT show superior performance on small datasets like Cora.</li><li>GraphSAGE exhibits impressive improvements in accuracy compared to GCN and GAT in the experiments.</li></ul>

Graph Neural Networks Part 3: How GraphSAGE Handles Changing Graph Structure

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