A little suggestion about global variables

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#1

I want build a model using graph’s global feature. But DGL only has node-update and edge-update functions. If you can contain global update function, that’s will be helpfull.

#2

Hi,

Could you please elaborate on the context where you want to only update the graph features? In most graph neural networks I’ve seen, the graph feature is a function of the node features and edge features. To update the graph feature is first to perform node/edge feature update and then to apply a function to them (what we call the “readout” of graphs). If you really only want to update graph features independent of the particular graph structure, I think you can simply treat it as some input to a model without using DGL. Hope this helps.

#3

Thanks for the question. One of the design philosophy of DGL is to take control of only the necessary. A global state is not tied to nodes or edges, so you could maintain it outside of DGL. Some demo codes:

g = DGLGraph(...)  # some graph with node features
glb_state = ...
def mfunc(edges):
  return {'m' : edges.src['h'] * glb_state}
for epoch in range(MAX_EPOCH):
  g.update_all(mfunc, ...)
  glb_state = SomeGlobalUpdateFunc(h)
  h = g.ndata['h']

See that glb_state is maintained as function closure outside of DGLGraph.