[Example][Refactor] Multi-gpu graphsage link prediction example refac…

…tor (dmlc#5091)

examples/pytorch/graphsage/advanced/README.md

@@ -1,39 +1,12 @@
 More Examples for Training GraphSAGE
 ============================
 
-### Pure GPU sampling
-
-```bash
-python3 pure_gpu_node_classification.py
-```
-
-### Unsupervised training
-
-Train w/ mini-batch sampling in an unsupervised fashion (on the Reddit dataset)
-```bash
-python3 train_sampling_unsupervised.py
-```
-
-Notably,
-
-* The loss function is defined by predicting whether an edge exists between two nodes or not.  This matches the official
-  implementation, and is equivalent to the loss defined in the paper with 1-hop random walks.
-* When computing the score of `(u, v)`, the connections between node `u` and `v` are removed from neighbor sampling.
-  This trick increases the F1-micro score on test set by 0.02.
-* The performance of the learned embeddings are measured by training a softmax regression with scikit-learn, as described
-  in the paper.
-
-Micro F1 score reaches 0.9212 on test set.
-
-This example also demonstrates the advanced usages of multi-GPU `DDP` training, UVA-base sampling, full GPU sampling, and fine control of storing the graph structure and features individually.
-
 ### Training with PyTorch Lightning
 
-We also provide minibatch training scripts with PyTorch Lightning in `train_lightning.py` and `train_lightning_unsupervised.py`.
+We provide minibatch training scripts with PyTorch Lightning in `train_lightning_unsupervised.py`.
 
 Requires `pytorch_lightning` and `torchmetrics`.
 
 ```bash
-python3 train_lightning.py
 python3 train_lightning_unsupervised.py
 ```