[Example] SEAL for OGBL (#4291)

* [Example] SEAL for OGBL

* update index

* update

* fix readme typo

* add seal sampler

* modify set ops

* prefetch

* efficiency test

* update

* optimize

* fix ScatterAdd dtype issue

* update sampler style

* update
Co-authored-by: Quan Gan <coin2028@hotmail.com>

examples/README.md

@@ -100,6 +100,9 @@ To quickly locate the examples of your interest, search for the tagged keywords
 - <a name="caregnn"></a> Dou Y, Liu Z, et al. Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. [Paper link](https://arxiv.org/abs/2008.08692).
     - Example code: [PyTorch](../examples/pytorch/caregnn)
     - Tags: Multi-relational graph, Graph neural network, Fraud detection, Reinforcement learning, Node classification
+- <a name="seal_ogbl"></a>  Zhang et al. Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. [Paper link](https://arxiv.org/pdf/2010.16103.pdf).
+    - Example code: [PyTorch](../examples/pytorch/ogb/seal_ogbl)
+    - Tags: link prediction, labeling trick, OGB
 
 ## 2019
 

examples/pytorch/ogb/seal_ogbl/README.md

+# SEAL Implementation for OGBL in DGL
+
+Introduction
+------------
+This is an example of implementing [SEAL](https://arxiv.org/pdf/2010.16103.pdf) for link prediction in DGL. Some parts are migrated from [https://github.com/facebookresearch/SEAL_OGB](https://github.com/facebookresearch/SEAL_OGB).
+
+Requirements
+------------
+[PyTorch](https://pytorch.org/), [DGL](https://www.dgl.ai/), [OGB](https://ogb.stanford.edu/docs/home/), and other python libraries: numpy, scipy, tqdm, sklearn, etc.
+
+Usages
+------
+Run the following command for results on each benchmark
+```bash
+# ogbl-ppa
+python main.py \
+    --dataset ogbl-ppa \
+    --use_feature \
+    --use_edge_weight \
+    --eval_steps 5 \
+    --epochs 20 \
+    --train_percent 5 
+
+# ogbl-collab
+python main.py \
+    --dataset ogbl-collab \
+    --train_percent 15 \
+    --hidden_channels 256 \
+    --use_valedges_as_input
+
+# ogbl-ddi
+python main.py \
+    --dataset ogbl-ddi \
+    --ratio_per_hop 0.2 \
+    --use_edge_weight \
+    --eval_steps 1 \
+    --epochs 10 \
+    --train_percent 5
+
+# ogbl-citation2
+python main.py \
+    --dataset ogbl-citation2 \
+    --use_feature \
+    --use_edge_weight \
+    --eval_steps 1 \
+    --epochs 10 \
+    --train_percent 2 \
+    --val_percent 1 \
+    --test_percent 1
+```
+
+Results
+-------
+
+|              | ogbl-ppa (Hits@100) | ogbl-collab (Hits@50) | ogbl-ddi (Hits@20) | ogbl-citation2 (MRRd) |
+|--------------|---------------------|-----------------------|--------------------|---------------------|
+| Paper Test Results |  48.80%±3.16% |    64.74%±0.43% | 30.56%±3.86%* |   87.67%±0.32r% |
+| Our Test Results |  49.48%±2.52% |    64.23%±0.57% | 27.93%±4.19% |   86.29%±0.47% |
+
+\* Note that the relatively large gap on ogbl-ddi may come from the high variance of results on this dataset. We get 28.77%±3.43% by only changing the sampling seed.
+
+Reference
+---------
+
+    @article{zhang2021labeling,
+        title={Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning},
+        author={Zhang, Muhan and Li, Pan and Xia, Yinglong and Wang, Kai and Jin, Long},
+        journal={Advances in Neural Information Processing Systems},
+        volume={34},
+        year={2021}
+        }
+
+    @inproceedings{zhang2018link,
+      title={Link prediction based on graph neural networks},
+      author={Zhang, Muhan and Chen, Yixin},
+      booktitle={Advances in Neural Information Processing Systems},
+      pages={5165--5175},
+      year={2018}
+    }
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