How to evaluate a pyTorch/DGL tensor

From a DGL graph I want to see the adjacency matrix with

adjM = g.adjacency_matrix()
adjM

and I get the following which is fine:

tensor(indices=tensor([[0, 0, 0, 1],
                       [1, 2, 3, 3]]),
       values=tensor([1., 1., 1., 1.]),
       size=(4, 4), nnz=4, layout=torch.sparse_coo)

Now I want to have the adjacency matrix and the node values each by itself. I imagine something of this kind:

adjMatrix = adjM.indices   # or
adjMatrix = adjM[0]
nodeValues = adjM.values   # or
nodeValues = adjM[1]

But this form is not estimated by pyTorch/DGL. My beginner's question:

• how to do this correctly and sucsessfully? and
• is there a tutorial for a nuby? ( I have searched a lot just for this detail...!)

Click here! You will find the usage of dgl.adj(). As the doc said, the return is an adjacency matrix, and the return type is the SparseTensor.

I noticed that the output that you post is a SparseTensor.

You can try it as follows then you can get the entire adj_matrix

I create a dgl graph g, get the adjacency matrix as adj

g = dgl.graph(([0, 1, 2], [1, 2, 3]))
adj = g.adj()
adj

output is:

tensor(indices=tensor([[0, 1, 2],
                       [1, 2, 3]]),
       values=tensor([1., 1., 1.]),
       size=(4, 4), nnz=3, layout=torch.sparse_coo)

We can find that adj is the presence of sparse, and the sparse type is coo, we can use the following code to verify if adj is a SparseTensor

adj.is_sparse

output :

True

so we can use to_dense() get the original adj matrix

adj.to_dense()

the result is:

tensor([[0., 1., 0., 0.],
        [0., 0., 1., 0.],
        [0., 0., 0., 1.],
        [0., 0., 0., 0.]])

When you have a problem with DGL you can check the Deep Graph Library Tutorials and Documentation.