GraphSAGE
GraphSAGE introduces a scalable and adaptive approach to graph representation learning, addressing some limitations of GCN and enhancing scalability. At its core, GraphSAGE employs a neighborhood sampling and aggregation strategy, diverging from the fixed-weight aggregation mechanism of GCN.
In GraphSAGE, the process of learning node representations involves iteratively sampling and aggregating information from local neighborhoods. Let 
be a graph with nodes 
and edges 
, and 
denote the embedding of node 
at layer 
. The update rule for GraphSAGE can be expressed as follows:
Here, 
represents a dynamically sampled subset of neighbors for node 
at each iteration. This adaptability allows GraphSAGE to scale more efficiently compared to GCN, especially in scenarios where the graph is large or when computational resources are limited, maintaining scalability.
The structure for PyG code remains the same as for the GCN; we will just be using the GraphSAGE module from...
