I have a question regading in Heterogeneous Graph Attention Network. The input graph is too large causing GPU out of memory.How can I split the graph into multiple subgraphs?
You can use neighbor sampling for mini-batch training of HAN. There’s a section in DGL’s user guide for mini-batch training of GNN on heterogeneous graphs for node classification.
Further discussion in https://github.com/dmlc/dgl/issues/2248 .
@BarclayII Has anyone solved this problem? I have met similar issues when I try to use our function
this discussion still don’t have any solution:
The original HAN algorithm is unfortunately not scalable on large graphs due to it’s tendency to significantly densify the graph. In overall, I don’t see a straightforward answer to that, which means it is probably an open question that requires research effort. As a result, I won’t treat it as a bugfix or a feature request. However, I would change my mind if there exist some work that present a reasonable upgrade (e.g., a mini-batch version) of HAN, and the team would be happy to learn from that.