Why is the Feature Matrix (X) sparse instead of the Adjacency Matrix (A)? about clustergcn HOT 3 CLOSED

line 40 in utils.py
features = coo_matrix((feature_values, (node_index, feature_index)), shape=(node_count, feature_count)).toarray()

The feature matrix (x) isn't sparse, instead, it's a dense matrix (ndarray) due the .toarray() method.

And the adjacency matrix (A) is represented as edge_index and edge_weight.

from clustergcn.

In that case, how to make a sparse adjacency matrix (A) given edge_index and edge_weight. My goal is to implement the GCNConv layer on my own by just using PyTorch.

from clustergcn.

You would like to call torch.sparse.FloatTensor(edge_index , edge_weight, shape), where shape denotes the dense shape of adjacancy matrix A.

For more details, you may refer to pygcn.

from clustergcn.