I want to ask that, is it possible to get the graph level output using this code. As you indicated in your code
Equation 7 of section 3.3
output = self.out(torch.cat((hidden_state, annotation), 2))
output = output.sum(2)
This must evaluate the graph level representation of the graph. But i think this outputs the node scores using the following equation described in the paper
o = g(h; x)
and then passing these scores in cross entropy loss where first softmax is applied and then the loss is evaluated. I am confused, can you please guide a bit on this aspect.
Moreover, if it provides a graph level representation vector, then for graph classification task i can take this vector as input to another neural network that will output the class the graph belongs to and that thing is not yet available with the current code?
I want to ask that, is it possible to get the graph level output using this code. As you indicated in your code
This must evaluate the graph level representation of the graph. But i think this outputs the node scores using the following equation described in the paper
o = g(h; x)and then passing these scores in cross entropy loss where first softmax is applied and then the loss is evaluated. I am confused, can you please guide a bit on this aspect.
Moreover, if it provides a graph level representation vector, then for graph classification task i can take this vector as input to another neural network that will output the class the graph belongs to and that thing is not yet available with the current code?