dmlc--dgl
Quan (Andy) Gan
e667545da5
* add seal example * 1. add paper infomation in examples/README 2. adjust codes 3. option test * use latest `to_simple` to replace coalesce graph function * remove outdated codes * remove useless comment * Node2vec 1.implement node2vec random walk c++ op 2.implement node2vec model 3.implement node2vec example * add CMakeLists file modify * refine c++ codes * refine c++ codes * add missing whitespace * refine python codes * add codes * add node2vec_impl.h * fix codes * fix code style problem * fixes * remove * lots of changes * add benchmark * fixes Co-authored-by: smilexuhc <smile.xuhc@gmail.com> Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
54 行
1.3 KiB
Markdown
54 行
1.3 KiB
Markdown
# DGL Implementation of the Node2vec
|
|
This DGL example implements the graph embedding model proposed in the paper
|
|
[node2vec: Scalable Feature Learning for Networks](https://arxiv.org/abs/1607.00653)
|
|
|
|
The author's codes of implementation is in [Node2vec](https://github.com/aditya-grover/node2vec)
|
|
|
|
|
|
Example implementor
|
|
----------------------
|
|
This example was implemented by [Smile](https://github.com/Smilexuhc) during his intern work at the AWS Shanghai AI Lab.
|
|
|
|
The graph dataset used in this example
|
|
---------------------------------------
|
|
|
|
cora
|
|
- NumNodes: 2708
|
|
- NumEdges: 10556
|
|
|
|
ogbn-products
|
|
- NumNodes: 2449029
|
|
- NumEdges: 61859140
|
|
|
|
|
|
Dependencies
|
|
--------------------------------
|
|
|
|
- python 3.6+
|
|
- Pytorch 1.5.0+
|
|
- ogb
|
|
|
|
|
|
How to run example files
|
|
--------------------------------
|
|
To train a node2vec model:
|
|
```shell script
|
|
python main.py --task="train"
|
|
```
|
|
|
|
To time node2vec random walks:
|
|
```shell script
|
|
python main.py --task="time" --runs=10
|
|
```
|
|
|
|
Performance
|
|
-------------------------
|
|
|
|
**Setting:** `walk_length=50, p=0.25, q=4.0`
|
|
| Dataset | DGL | PyG |
|
|
| -------- | :---------: | :---------: |
|
|
| cora | 0.0092s | 0.0179s |
|
|
| products | 66.22s | 77.65s |
|
|
Note that the number in table are the average results of multiple trials.
|
|
For cora, we run 50 trials. For ogbn-products, we run 10 trials.
|