[bug] DGNConv fails when no directional aggregators is used

[rudongyu]

🐛 Bug

DGNConv is not working when no directional aggregators is given. The message function of DGNConvTower tries to retrieve eigen vector node features that do not exist.

To Reproduce

import dgl
import torch as th
from dgl.nn import DGNConv
g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
feat = th.ones(6, 10)
conv = DGNConv(10, 10, ['sum'], ['identity'], 2.5)
ret = conv(g, feat)

Expected behavior

The message function only passes other related node features when no directional aggregators is given.

Environment

• DGL 0.9.0:
• PyTorch 1.12.0+cu113

[mufeili]

Do we expect this behavior? Perhaps in that case users should use PNAConv instead?

[rudongyu]

Do we expect this behavior? Perhaps in that case users should use PNAConv instead?

We allow eig_vec to be None in its forward method. It's necessary to check it before message passing. Plus, it could be a redundant feature for use cases like ablation study on DGNConv. What do you think?

[mufeili]

1. If the authors proposed a model architecture without eig_vec, then we just need to implement that.
2. If the extension to the case of None for eig_vec is extremely natural, then we can extend it ourselves.
3. Otherwise, we can make this a non-optional argument.