09e572cef1dc655bfc5255ecd0b3787512609e3d,torch_geometric/nn/functional/graph_conv.py,,graph_conv,#Any#Any#Any#Any#Any#,27

Before Change

    edge_attr *= degree[col]
    edge_attr = torch.cat([edge_attr, degree * degree], dim=0)
    edge_index = add_self_loops(edge_index, n)
    adj = SparseTensor(edge_index, edge_attr, torch.Size([n, n]))

    // Convolution.
    output = SparseMM(adj)(torch.mm(x, weight))

After Change

    row, col = edge_index
    num_nodes, e = x.size(0), row.size(0)

    edge_attr = x.new_full((e, ), 1) if edge_attr is None else edge_attr
    deg = degree(row, num_nodes, dtype=x.dtype, device=x.device).pow_(-0.5)

    // Normalize and append adjacency matrix by self loops.

Instances