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Hi Thomas,
I'm confused when you generate the negative edge labels of validation set as:
val_edges_false = []
while len(val_edges_false) < len(val_edges):
idx_i = np.random.randint(0, adj.shape[0])
idx_j = np.random.randint(0, adj.shape[0])
if idx_i == idx_j:
continue
if ismember([idx_i, idx_j], train_edges):
continue
if ismember([idx_j, idx_i], train_edges):
continue
if ismember([idx_i, idx_j], val_edges):
continue
if ismember([idx_j, idx_i], val_edges):
continue
if val_edges_false:
if ismember([idx_j, idx_i], np.array(val_edges_false)):
continue
if ismember([idx_i, idx_j], np.array(val_edges_false)):
continue
val_edges_false.append([idx_i, idx_j])However, the test negative set is confirmed by
if ismember([idx_i, idx_j], edges_all):
continueWhy does validation set use ismember([idx_j, idx_i], train_edges) and ismember([idx_i, idx_j], val_edges) instead of ismember([idx_i, idx_j], edges_all)?
Wu Shiauthie
lif323
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