I am trying to implement a graph classification experiment using Graph Convolutional Networks (GCN) in DGL. I have read the GCN example in DGL but that example uses the Citation-Graph dataset (Cora, PubMed, …) which only has a single large graph.
I have implemented my own dataset class, which loads a list of DGL graphs (
self.dgl_graphs) and list of associated labels (
self.labels) and contains the following methods:
__len__(self): returns the number of graphs
__getitem__(self, i): returns a pair
This is a binary-classification problem so the labels for each sample graph is either
1. Each sample graph has tens of nodes/edges and each node has an associated tensor.
Can someone help me by providing the minimal code to train and evaluate this task using the built-in GCN module in DGL (
If GCN is not suitable for this task, please provide a minimal code based on another preferred built-in NN module in DGL.