Graph-level message passing requires operating on the entire graph and all node features simultaneously, which may not be feasible due to memory limitations. In addition, using the graph-level equations essentially limits one to full-batch (as opposed to minibatched) gradient descent.


University of Michigan - Ann Arbor

To use mini-batch training, one can work with a subset of nodes during message passing.

Subsampling of Graph-level Message Passing

Hamilton, W.L. Graph Representation Learning. *Synthesis Lectures on Artificial Intelligence and Machine Learning*. 2020, 14(3), 1–159. https://doi.org/10.2200/S01045ED1V01Y202009AIM046

Graph Representation Learning by William Hamilton

Why Subsampling of Graph-level Message Passing is Needed?

We can not run message passing on a subset without losing information. Because when we remove nodes, we also delete edges.

Challenge of Subsampling of Graph-level Message Passing

https://arxiv.org/abs/1706.02216?context=cs

Inductive representation learning on large graphs. 

First select a set of target nodes for a batch and then to recursively sample the neighbors of these nodes in order to ensure that the connectivity of the graph is maintained.

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