I’m doing some research for a project and the dataset I’m using happens to be graphs. DGL has been a great resource thus far and I currently have a neural network running that basically mimics this tutorial Training a GNN for Graph Classification — DGL 0.6.1 documentation.
I have a couple of questions thus far. The data set I am using may be unique when compared to other examples because every graph has exactly the same nodes but the connections/adjacency are different from graph to graph. As a simplified example, let’s say we have nodes x1, x2, and x3. In graph 1, x1 is directed towards x2 and x2 towards x3. In graph 2, both x1 and x3 are directed towards x2. The important thing is that x1, x2, and x3 are exactly the same node in every graph but they are connected differently. I am wondering how I should approach such a task and if the above tutorial is accounting for something like this because otherwise I can imagine that the data looks extremely repetitive to the neural network and it may be unable to actually learn much of anything?
My second question is regarding hidden layers. The graphs I am using have no features whatsoever. There is only the graph and the class it is in. With that being said, what do the hidden layers look like? The above tutorial does not explain and I’m really curious how the hidden layers are working in an example like mine where there are no features. Is a graph convolutional network the appropriate method to tackle this kind of problem?
Sorry for the lengthy questions and thank you!