I'm attempting to use get_edges_prob to find the most likely missing edges out of every possible non-edge. I know every possible edge is O(n^2).
Currently I'm sampling the like this:
non_edges_probs = [ for _ in range(len(non_edges))]
s = s.levels
for i, non_edge in enumerate(non_edges):
p = s.get_edges_prob([non_edge], ,
Is there a way to speed this up at all? If not, is there a heuristic I can use to reduce the number of possibilities?
Currently I'm using vertex similarity measures to cut the possibilities, but I'm wondering if there is a heuristic involving just the NestedState.
Any help appreciated!