# PeriodicMove

**Repository Path**: mcdragon/PeriodicMove

## Basic Information

- **Project Name**: PeriodicMove
- **Description**: This is the source code for “PeriodicMove: Shift-aware Human Mobility Recovery with Graph Neural Network” accepted by CIKM 2021.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-11-08
- **Last Updated**: 2023-06-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PeriodicMove: Shift-aware Human Mobility Recovery with Graph Neural Network
This is the source code for “PeriodicMove: Shift-aware Human Mobility Recovery with Graph Neural Network” accepted by CIKM 2021: 

## Dependencies

- Python==3.6
- torch==1.5.0
- transformers==2.9.0
- easydict==1.9
- matplotlib==3.1.1

## Running

For the simplification, we only supply the foursquare dataset used in the paper. 

```
Foursquare_mask_num_10.tar.gz
```

Firstly, the user should extract the file, e.g., pos.vocab.txt. Then they need to change the setting in the config.

```python
vocab_path      # the path of vocab file
dist_path       # the path of distance file
train_file      # the path of traning data
eval_path       # the path of validation data
test_path       # the path of testing data
save_dir        # the path for saving model and embedding matrix
```

After that, type the following command in the termination.

```bash
python main.py
```

When the training procedure is completed, the terminal will print the results stated in our paper.