Hi @mufeili thanks for your past guidance. I come now with new questions.

**Context**

I’m interested in using a single GNN to perform binary node classification across multiple heterographs.

I have a set of ~4000 bipartite graphs, with nodes a and b each with 2000 connections.

These nodes have different feature spaces (a’ and b’) but share a common label, which is binary.

I’m trying to perform binary classification on the nodes in these graphs.

So that my model can then predict labels on new unseen graphs where there are no labels but all node features are present.

I’ve read the discussion on node classification over multiple graphs found here.

As a result I have experimented with batching and the custom collate functions as necessary.

However I’ve failed to translate that training practice to heterographs.

This is complicated by a lack of examples of heterographs that use node features as apposed to learnable embeddings.

**Questions**

1 - How do I alter the HeteroRGCN model here to allow for the usage of node features?

2 - Should I be batching my heterographs or simply iterating over the graph dataset?

3 - Given the graph structure below what’s the best way to structure my training loop? Are there any good examples I’ve missed?

4 - Do you have any recommended readings for my setting? I’ve discovered 1 and 2 so far.

**Heterograph Construction**

*This is all wrapped up in a DGLDataset object, such that get_item returns dgl_hetero_graph.*

dgl_hetero_graph = dgl.heterograph({('a, ‘link’, ‘b’): (u, v)})

dgl_hetero_graph.nodes[‘a’].data[‘a_feat’] = torch.tensor(‘a_feat’), dtype=torch.float32)

dgl_hetero_graph.nodes[‘a’].data[‘a_labl’] = torch.tensor(‘a_labl’), dtype=torch.float32)

dgl_hetero_graph.nodes[‘b’].data[‘b_feat’] = torch.tensor(‘b_feat’), dtype=torch.float32)

dgl_hetero_graph.nodes[‘b’].data[‘b_labl’] = torch.tensor(‘b_labl’), dtype=torch.float32)

Thanks again