GraphSAGE
GraphSAGE is an inductive graph-neural-network method that learns node representations by sampling and aggregating information from node neighborhoods.
Graph Neural Networks in Action
GraphSAGE is a graph neural network method that learns node representations by aggregating sampled neighborhood information.
- How it is used
- Predicts product categories from a co-purchase graph and produces embeddings for exploring potential product bundles.
- How deep it goes
- Works through sampled-neighbor updates and a two-layer implementation, then examines aggregation settings and selected SAGEConv operations.
- Examples
- Uses an Amazon co-purchase graph, PyTorch Geometric model classes, aggregation comparisons, and a classification confusion matrix.
- What you can do after
- Apply it via PyTorch Geometric to train a node classifier and evaluate aggregation choices against classification metrics.
Read how Graph Neural Networks in Action explains GraphSAGE →
Knowledge Graphs and LLMs in Action
GraphSAGE is an inductive graph representation-learning framework that generates node representations by aggregating information from sampled neighborhoods.
- How it is used
- Deploys neighborhood aggregation for node classification and link prediction in financial fraud detection and movie recommendation pipelines.
- How deep it goes
- Implements a two-layer encoder with feature transformation, ReLU, and neighborhood aggregation, without detailing SAGE's internal sampling procedure.
- Examples
- Uses transaction graphs for licit-versus-illicit classification and user–movie graphs for recommendation link prediction.
- What you can do after
- Builds a PyTorch Geometric SAGE encoder and applies it within classification or recommendation evaluation workflows.
Read how Knowledge Graphs and LLMs in Action explains GraphSAGE →
Related concepts
- graph convolutional networkcontrasts with · A graph convolutional network is a graph neural network that computes representations by transforming and aggregating information from graph neighborhoods.
- graph samplinguses · Graph sampling selects subsets of graph nodes, edges, or neighborhoods for computation.
- SAGEConvuses · SAGEConv is a PyTorch Geometric graph convolution layer that updates node representations by aggregating information from neighboring nodes.
- graph neural networkkind of · A graph neural network is a neural-network model that learns representations or predictions from graph-structured data by combining information from connected entities.
- graph attention networkcontrasts with · A graph attention network is a graph neural network that uses attention weights to aggregate information from neighboring nodes, allowing their contributions to differ.
- precisionmeasures · Precision is the proportion of items identified as positive, selected, or retrieved that satisfy the relevant correctness or relevance criterion.
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