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Hi OGB-Team,
right now, heterogenous graphs are loaded as a Data objects by filling x_dict, edge_index_dict etc.. HeteroData in PyG on the other hand, stores features and edges by its node-type or edge-type identifier, e.g. node-related buffers like x are exposed as data[NODE_TYPE].buffer and edge-related buffers like edge_index are exposed as data[EDGE_TYPE].buffer.Those representations are quite similar besides basically the order of keys accessing the data, e.g. data.x_dict[NODE_TYPE] vs data[NODE_TYPE].x.
Is there any particular reason for this? If not, HeteroData would seem more flexible to me in the PyG framework, as it would allow for direct use with samplers or for transformations like to_undirected or to_homogeneous.
The text was updated successfully, but these errors were encountered:
This is a good suggestion! For now, it should be no problem to manually convert your data into a HeteroData object but I agree that there exists no reason to not integrate PyG 2.0 features within OGB. I will try to do this.
Hi OGB-Team,
right now, heterogenous graphs are loaded as a
Data
objects by fillingx_dict
,edge_index_dict
etc..HeteroData
in PyG on the other hand, stores features and edges by its node-type or edge-type identifier, e.g. node-related buffers likex
are exposed asdata[NODE_TYPE].buffer
and edge-related buffers likeedge_index
are exposed asdata[EDGE_TYPE].buffer
.Those representations are quite similar besides basically the order of keys accessing the data, e.g.data.x_dict[NODE_TYPE]
vsdata[NODE_TYPE].x
.Is there any particular reason for this? If not,
HeteroData
would seem more flexible to me in the PyG framework, as it would allow for direct use with samplers or for transformations liketo_undirected
orto_homogeneous
.The text was updated successfully, but these errors were encountered: