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View Code? Open in Web Editor NEWImplementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"
Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"
I tried to use my own data but don't know the way.
I want to reproduce the result on the PubMed dataset with a 0.00 perturbation rate. However, I can't get the same results recorded in your paper while I just modify the ptb_rate to 0 in the .sh file. Does there need another hyperparameters setting since the providing .sh file corresponds to ptb_rate = 0.15?
Argument 'hidden' controls the vector dimension of nodes? As you run the graph (adj matrix) through the gcn
The resulting size of the node vectors is the hidden value (128) or is it adjusted by the features as well?
Would this code accommodate a graph with edge weights? Ideally I would want to use edge weights in the GCN adjacency matrix instead of '1's and '0's.
Hello, I would like to ask about the reproduce of Table 1. How do I reproduce the results? Generate the adjacency matrix and calculate the percentage by myself or am I missing something?
About ‘Adaptive Integration’,in which file in the code is this function implemented,‘model’ or ‘layer’?
And in which methods is it implemented?Please tell me ,and thank you for your answer.
According to my understanding, in line 190-192 of model_simp.py:
" for i in range(len(self.midlayer)):
midgc = self.midlayer[i]
x = midgc(x, adj)"
"x" represents P^(l) in the paper, but I am confused about operation "midgc(x, adj)". Can you tell me which formula that "midgc(x, adj)" corresponds to in the paper?
Thanks!
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