Local pooling in graph regression/classification problems

[msainsburydale]

Is it possible to use local pooling between the layers of a graph neural network, so that clusters of nodes are aggregated between graph convolution layers?

[CarloLucibello]

Do you have a specific paper in mind?

[msainsburydale]

Thanks for your quick reply. Understanding Pooling in Graph Neural Networks sparked my interest. The authors introduce a framework "SRC" for unifying local pooling operators discussed in the literature: (S)elect K subsets of nodes that will be aggregated, (R)educe over these node subsets (i.e., pool over them), and (C)onnect the aggregated nodes to form a new graph with K nodes.

Personally, I'm interested in applying a local pooling layer that is as similar as possible to conventional pooling found in CNNs. For context, my graph input is a spatial data set, edge features are the spatial distance between observations, and I'm performing a graph-level regression task. So, I thought a simple K-means clustering approach would be effective, or the Graclus algorithm referenced in the above paper:

Graclus halves the node set by iteratively collapsing a randomly selected node with its most strongly connected neighbour. The method is equivalent to more expensive approaches based on the eigendecomposition of the adjacency matrix.

Thanks in advance for any comments you may have.

[CarloLucibello]

It seems hard to produce a gpu-friendly implementation of some of those operators, but one can start with cpu-only implementations. I made a list of some of those pooling operators in #308