Comments (1)
Sometimes the graph size is so large that it is impractical to loop through the entire graph via mini-batches in every epoch. So instead of setting the number of training epochs, we intend to set the number of training batches in model.train() and expect the model to exhibit inductive ability.
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Related Issues (20)
- DistMult running error HOT 1
- Can you add the weight information into link.dat.test? HOT 1
- Cannot assign node feature "h" on device cuda:0 to a graph on device cpu. HOT 3
- R-GCN RuntimeError: Found dtype Long but expected Float HOT 1
- Error in MAGNN HOT 2
- dgl version conflict HOT 1
- Data missing HOT 1
- unsupervised training with HAN and MAGNN HOT 2
- MAGNN training memory consumption HOT 2
- May I know how you find the best hyperparameters for the unsupervised task?
- Is it possible to train HAN on weighted graph? HOT 1
- HAN kill! HOT 4
- What does the meta='1, 2, 4, 8' mean in HAN run.sh file?
- Segmentation Fault [TransE] HOT 1
- Dimension out of range (expected to be in range of [-1, 0], but got -2) [HGT] HOT 5
- About config.dat in HAN HOT 2
- Data
- About source of HAN HOT 1
- DistMult running error HOT 2
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