Heterograph models accounting for note attributes

From my understanding of the heterograph model:
The model provided does not support node features (the graph structure is what is input) as indicated by the following excerpt from the above tutorial:
“Create a simple GNN by stacking two HeteroRGCNLayer . Since the nodes do not have input features, make their embeddings trainable…”
Is there a model that considers input features, i.e., I can add features/properties to the node.


You may find this example helpful: https://github.com/dmlc/dgl/tree/master/examples/pytorch/han.

The example works with a heterogeneous graph with one type of nodes and multiple types of edges and can be extended to a graph with multiple types of nodes. This example has considered initial node features for DGLHeteroGraph.

Thank you. I’ll go through the example.

Hi Mufei,
That you for that pointer. I did go through it and the metapath2vec paper. I have the following question. How do you generate the metapaths for a heterogeneous graph - general. In the example that you have here, since there are only two heterogeneous nodes and the feature, the meta-path scheme is obvious. In general, how do you know the meta-paths for a heterogeneous graph. From https://i.cs.hku.hk/~ckcheng/papers/www15-metapath.pdf , it looks like this is an area of research. Do you have any pointers to share. For my particular problem, I am going to reduce it to two heterogeneous nodes like your example and go with it.

@rajiv I’m not an expert in this area so I guess I cannot help much. In the most naive case like HAN, this can be done by raising the adjacency matrices to a power of k, i.e. if I can reach node B from node A within k steps and each step happens on a particular edge type, then A and B are considered to be on the same metapath.

Thanks, Mufei. Appreciate the help.