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Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* The start of experiments of Jiahang Li on GraphSAINT.

* a nightly build

* a nightly build

Check the basic pipeline of codes. Next to check the details of samplers , GCN layer (forward propagation) and loss (backward propagation)

* a night build

* Implement GraphSAINT with torch.dataloader

There're still some bugs with sampling in training procedure

* Test validity

Succeed in testing validity on ppi_node experiments without testing other setup.
1. Online sampling on ppi_node experiments performs perfectly.
2. Sampling speed is a bit slow because the operations on [dgl.subgraphs], next step is to improve this part by putting the conversion into parallelism
3. Figuring out why offline+online sampling method performs bad, which does not make sense
4. Doing experiments on other setup

* Implement saint with torch.dataloader

Use torch.dataloader to speed up saint sampling with experiments. Except experiments on too large dataset Amazon, we've ...

* graphsaint

* graphsaint

* graphsaint

* graphsaint

* fixed the model

* fixed some bugs

* fixed the computing of normalization and updated the results

* fixed some bugs and updated the results

* Update utils.py

* Update train_sampling.py

* Update train_sampling.py
Co-authored-by: Mufei Li <mufeili1996@gmail.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>