data = Data.load('cora')
data.print_statisitcs()

# data randomly sample training nodes and validation nodes for experiments
# 20 is the num. training nodes for EACH CLASS, 500 is the num. validation nodes among ALL nodes.
data.split_setting = [20, 500] # or you can modify split.txt in /data/{dataset_name} to set this
data.update_mask()

# accessing adjacecny matrix(torch.Tensor)
A = data.raw_adj.to_dense() # data.adj is pytorch sparse tensor

# accessing normalied adjacency matrix(torch.Tensor)
norm_A = data.norm_adj.to_dense()

# features(torch.Tensor)
data.features

# labels(torch.Tensor)
data.labels