Random adjacency matrices do not affect the performance
zhanqiuzhang opened this issue · comments
Hi, thanks for sharing the code!
I find that after randomly breaking the adjacency matrices, the performance of CompGCN remains unchanged (0.334, DistMult+multiplication). The codes in run.py that I have changed are as follows.
for sub, rel, obj in self.data['train']:
obj = random.randint(0, self.p.num_ent)
edge_index.append((sub, obj))
edge_type.append(rel)
# Adding inverse edges
for sub, rel, obj in self.data['train']:
obj = random.randint(0, self.p.num_ent)
edge_index.append((obj, sub))
edge_type.append(rel + self.p.num_rel)
Did I have any misunderstanding about the codes?
I think the main contribution to performance is the decoder. I tried to remove convolution layer but still obtain similar results