GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data.
The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and an unsupervised graph representation learner.
The project covers broader capabilities in machine learning for graph data, including supervised node classification and unsupervised embedding training. These processes utilize neighborhood aggregation strategies such as mean, max-pooling, or gated operations to transform node attributes into low-dimensional vector representations.
