How deeply does Knowledge Graphs and LLMs in Action cover GraphSAGE?

Implements a two-layer encoder with feature transformation, ReLU, and neighborhood aggregation, without detailing SAGE's internal sampling procedure.

GraphSAGE

As explained in Knowledge Graphs and LLMs in Action

GraphSAGE is an inductive graph representation-learning framework that generates node representations by aggregating information from sampled neighborhoods.

GraphSAGE is a graph neural network encoder that generates node representations by aggregating neighborhood features. It supports downstream node classification and link prediction through an encoder–decoder pipeline. section 12.0

A practical GraphSAGE pipeline follows these steps:

Represent a domain as a graph, such as transactions connected by Bitcoin flows or users connected to movies through ratings. section 12.0
Apply two SAGEConv layers: transform input features, apply ReLU, and produce output node representations. section 12.1.4
Pass representations to a task-specific decoder, such as log_softmax with CrossEntropyLoss for node classification. section 12.1.4
Evaluate predictions with precision, recall, F1-score, and confusion matrices, while comparing parameter counts and training time. section 12.1.5 section 12.2.5

AML node classification

A two-layer SAGE encoder processes a homogeneous transaction graph and produces representations used to classify each transaction as licit or illicit. In the reported comparison, SAGE used 5,427 parameters and trained in 36.71 seconds after 400 epochs, between GCN and GAT in efficiency.

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Graph Neural Networks in ActionGraphSAGE is a graph neural network method that learns node representations by aggregating sampled neighborhood information.

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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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