GraphSAGE is an inductive framework for learning node embeddings in large graphs. Unlike traditional methods that require all nodes to be present during training, GraphSAGE uses node features (e.g., text attributes) to generate embeddings for unseen nodes. It learns a function to aggregate features from a node's local neighborhood, enabling generalization to new graphs. The algorithm outperforms strong baselines on three inductive node-classification benchmarks: classifying unseen nodes in evolving information graphs using citation and Reddit data, and generalizing to completely unseen graphs using protein-protein interaction data. GraphSAGE uses a combination of node features and structural information to generate embeddings, and it can be trained in both unsupervised and supervised settings. The algorithm is efficient and scalable, with a focus on feature-rich graphs. It uses aggregator functions to combine features from a node's neighborhood, and different aggregator architectures (mean, LSTM, pooling) are evaluated. The algorithm is shown to effectively capture structural information about a node's role in a graph, even though it is based on features. GraphSAGE is compared against several baselines, including DeepWalk, and is found to outperform them in terms of accuracy and efficiency. Theoretical analysis shows that GraphSAGE can approximate clustering coefficients to arbitrary precision. The algorithm is implemented in TensorFlow and tested on three benchmark tasks: classifying academic papers, Reddit posts, and protein functions. The results show that GraphSAGE consistently outperforms baselines, with significant improvements in classification performance. The algorithm is efficient and scalable, with a focus on inductive learning and generalization across graphs.GraphSAGE is an inductive framework for learning node embeddings in large graphs. Unlike traditional methods that require all nodes to be present during training, GraphSAGE uses node features (e.g., text attributes) to generate embeddings for unseen nodes. It learns a function to aggregate features from a node's local neighborhood, enabling generalization to new graphs. The algorithm outperforms strong baselines on three inductive node-classification benchmarks: classifying unseen nodes in evolving information graphs using citation and Reddit data, and generalizing to completely unseen graphs using protein-protein interaction data. GraphSAGE uses a combination of node features and structural information to generate embeddings, and it can be trained in both unsupervised and supervised settings. The algorithm is efficient and scalable, with a focus on feature-rich graphs. It uses aggregator functions to combine features from a node's neighborhood, and different aggregator architectures (mean, LSTM, pooling) are evaluated. The algorithm is shown to effectively capture structural information about a node's role in a graph, even though it is based on features. GraphSAGE is compared against several baselines, including DeepWalk, and is found to outperform them in terms of accuracy and efficiency. Theoretical analysis shows that GraphSAGE can approximate clustering coefficients to arbitrary precision. The algorithm is implemented in TensorFlow and tested on three benchmark tasks: classifying academic papers, Reddit posts, and protein functions. The results show that GraphSAGE consistently outperforms baselines, with significant improvements in classification performance. The algorithm is efficient and scalable, with a focus on inductive learning and generalization across graphs.