In DGL, g.ndata[x]
represents the node features of the graph g
, where x
is the key of the feature. Is these node features trainable?
It can be trainable since it is just a Torch tensor, not sure whether it answers your question or not, feel free to follow up.
thanks for your reply! I observed that the node features in the GraphSAGE example come with the dataset. However, in my scenario, nodes don’t have original features. Therefore, I need to initialize learnable features, such as g.ndata[“feat”] = torch.nn.Parameter(torch.nn.init.xavier_uniform_(torch.empty(g.num_nodes(), args[“emb_dim”]))).to(args[“device”]). Is this correct?
Yes, it is correct. The parameter should be trainable.
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