Focusing on multiple labels (overlapping communities) is great! It can also be similar to changing the problem to edge labeling, instead of node labeling. The

Focusing on multiple labels (overlapping communities) is great! It can also be similar to changing the problem to edge labeling, instead of node labeling. The way we handle the common cases of entity overlap like in event data is another simple way: hypergraph modeling. In terms of reusing graph tech, that just means you make a bipartite graph between samples/events and features. That is one of the most common data transforms folks toggle on/off with Graphistry visuals. I'm guessing still another couple of years before a notion of standard practice emerges for graph learning. Very cool time!