@zihao, @HQ01 thanks both for your responses!
As far as clustering algorithms go, spectral clustering was the first thing I had in mind. What is perhaps just as important would be functionality to aggregate a graph according to the output of the clustering (whether by mean- or max-pool) and ideally disaggregating the graph as well (i.e. pass the features from the aggregate/cluster nodes to the initial nodes).
I’m more referring to clustering purely by graph structure. For the use case, the primary one would be a Unet-like model that can make node-level predictions and take into account higher-resolution properties of the input graph. A secondary one could be making graph level predictions (whether regression or classification) where going progressively to a smaller graph would be beneficial.