README.md

# Distributed metapath2vec in PGL
[metapath2vec](https://ericdongyx.github.io/papers/KDD17-dong-chawla-swami-metapath2vec.pdf) is a algorithm framework for representation learning in heterogeneous networks which contains multiple types of nodes and links. Given a heterogeneous graph, metapath2vec algorithm first generates meta-path-based random walks and then use skipgram model to train a language model. Based on PGL, we reproduce metapath2vec algorithm in distributed mode.


### Datasets
DBLP: The dataset contains 14376 papers (P), 20 conferences (C), 14475 authors (A), and 8920 terms (T). There are 33791 nodes in this dataset.
You can dowload datasets from [here](https://github.com/librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding)

We use the ```DBLP``` dataset for example. After downloading the dataset, put them, let's say, in ```./data/DBLP/``` .

### Dependencies
- paddlepaddle>=1.6
- pgl>=1.0.0

### How to run
Before training, run the below command to do data preprocessing.
```sh
python data_process.py --data_path ./data/DBLP  --output_path ./data/data_processed
```

We adopt [PaddlePaddle Fleet](https://github.com/PaddlePaddle/Fleet) as our distributed training frameworks. ```config.yaml``` is a configure file for metapath2vec hyperparameters and ```local_config``` is a configure file for parameter servers of PaddlePaddle. By default, we have 2 pservers and 2 trainers. One can use ```cloud_run.sh``` to help startup the parameter servers and model trainers. 

For examples, train metapath2vec in distributed mode on DBLP dataset.
```sh
# train metapath2vec in distributed mode.
sh cloud_run.sh

# multiclass task example
python multi_class.py --dataset ./data/data_processed/author_label.txt --ckpt_path ./checkpoints/2000 --num_nodes 33791

```

### Model Selection
Actually, There are 3 models in this example, they are ```metapath2vec```, ```metapath2vec++``` and ```multi_metapath2vec++```. You can select different models by modifying ```config.yaml```.

In order to run ```metapath2vec++``` model, you can easily rewrite the hyper parameter of **neg_sample_type** to **m2v_plus**, then ```metapath2vec++``` model will be selected.

```multi-metapath2vec++``` means that you are not only use a single metapath, instead, you can use several metapaths at the same time to train the model. For example, you might want to use ```c2p-p2a-a2p-p2c``` and  ```p2a-a2p``` simultaneously. Then you can rewrite the below hyper parameters in ```config.yaml```.
- **neg_sample_type**: "m2v_plus"
- **walk_mode**: "multi_m2v"
- **meta_path**: "c2p-p2a-a2p-p2c;p2a-a2p"
- **first_node_type**: "c;p"

### Hyperparameters
All the hyper parameters are saved in ```config.yaml``` file. So before training, you can open the config.yaml to modify the hyper parameters as you like.

Some important hyper parameters in ```config.yaml```:
- **edge_path**: the directory of graph data that you want to load
- **lr**: learning rate
- **neg_num**: number of negative samples.
- **num_walks**: number of walks started from each node
- **walk_len**: walk length
- **meta_path**: meta path scheme