GraphSage的所有node的embedding都相似度非常接近

1亿规模的节点，30亿左右的边，训练GraphSage模型。
训练参数如下：
```
--max_id 90170696
--feature_idx 0
--feature_dim 1
--learning_rate 0.00005
--num_epochs 1
--dim 128
--batch_size 128
```

首先，模型的收敛性并不好，loss没怎么下降
```
INFO:tensorflow:loss = 437.71362, mrr = 0.39257812, step = 1488562
INFO:tensorflow:loss = 413.18103, mrr = 0.46171877, step = 1488919 (2.109 sec)
INFO:tensorflow:loss = 449.76367, mrr = 0.4251302, step = 1489304 (2.003 sec)
INFO:tensorflow:loss = 422.31223, mrr = 0.43528646, step = 1489705 (2.045 sec)
....
INFO:tensorflow:loss = 439.37802, mrr = 0.4332031, step = 1601393 (2.210 sec)
INFO:tensorflow:loss = 495.66333, mrr = 0.47460938, step = 1601788 (2.124 sec)
INFO:tensorflow:loss = 411.83685, mrr = 0.47942704, step = 1602187 (2.181 sec)
INFO:tensorflow:loss = 434.9126, mrr = 0.4126302, step = 1602586 (2.162 sec)
.....
INFO:tensorflow:loss = 432.3944, mrr = 0.45585936, step = 3520258 (2.242 sec)
INFO:tensorflow:loss = 419.18842, mrr = 0.4451823, step = 3520649 (2.169 sec)
INFO:tensorflow:loss = 439.15546, mrr = 0.3761719, step = 3521049 (2.237 sec)
INFO:tensorflow:loss = 420.41013, mrr = 0.40924478, step = 3521450 (2.228 sec)
INFO:tensorflow:loss = 425.3105, mrr = 0.4282552, step = 3521840 (2.208 sec)
```
其次，获得的Embedding，任意两个node的Embedding相似度超过0.96。

Start by reproducing GraphSage training with the listed parameters and reviewing the reported loss, MRR, and embedding similarities. Compare the training behavior with the reported result that loss barely decreases and arbitrary node embeddings have similarity above 0.96; the issue is done when the cause is identified and the resulting embeddings and convergence meet an agreed expectation.

Written by the indexing model from the issue text.