Comments (1)
Thanks for transferring it to pytorch.
As to the minibatch, I suggest developing only one minibatch version for GCN, so that if you do not want to use minibatch, i.e. the original version, you just need to define the batch size as the total number of nodes in the graph.
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Related Issues (20)
- Test accuracy on Cora, Citeseer, and Pubmed using original split HOT 5
- converting npz file from reddit json file
- Graphsage data prep for large datasets HOT 1
- Different weights for calculating probability
- Running on Reddit dataset is extremely slow HOT 3
- Error in transformRedditGraph2NPZ.py HOT 4
- x tx files not opening HOT 1
- Embeddings
- for create_Graph.py bug
- The graph of FastGCN contains still the vertices of test, why the FastGCN is inductive? HOT 2
- Why not using the GPUs to train ? HOT 1
- How can gcn (batched) reduce memory usage? HOT 4
- `TypeError: 633 is not JSON serializable` when calling `create_Graph_forGraphSAGE.py` HOT 1
- Is this a one-layer version ? HOT 1
- no such folder named (./data/cora_graphSAGE) HOT 1
- Some question about CGNF HOT 2
- Setting of batch size and sampled layer size for large network
- If I want to see FastGCN's results on the Pumed dataset, ask which.py file to select?pubmed-original_inductive_FastGCN.py?
- how can I use it on my own dataset ?
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