Difference of the model design.
It seems the difference is that GraphSAGE sample the data.
But what is the difference in model architecture.
Topic knowledge-graph gnn deep-learning neural-network
Category Data Science
The main novelty of GraphSAGE is a neighborhood sampling step (but this is independent of whether these models are used inductively or transductively). You can think of GraphSAGE as GCN with subsampled neighbors.
In practice, both can be used inductively and transductively.
The title of the GraphSAGE paper ("Inductive representation learning") is unfortunately a bit misleading in that regard. The main benefit of the sampling step of GraphSAGE is scalability (but at the cost of higher variance gradients).
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