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It's super useful in practice!
In the table -> hypergraph transform @ https://github.com/graphistry/pygraphistry , we do `hypergraph(multicolumn_table, direct=True | False)['graph'].plot()` , which renders hypergraphs as a regular graph, this lets you pick/. Consider exploring some logs of customer activity or security events:
A hyperedge becomes either:
- a node of a bipartite graph. Ex: each log event becomes a node connecting the various entity nodes it mentions (IPs, accounts, countries, ...)
- .. or a bunch of pairwise entity<>entity edges. Ex: connect each IP<>account<>country directly, and label each edge with the hyperedge it came from.
In both cases, you can now directly leverage a lot of traditional graph thinking, and in our case, GPU acceleration.
Other systems might render hyperedges as say circles encomposing their nodes, but that's trickier at even small/medium scales
I increasingly just directly equate 'logs' with 'hypergraphs' and skip the relational step :)