Predicting Human Mobility via Graph Convolutional Dual-attentive Networks

Code implementation of the paper: Predicting Human Mobility via Graph Convolutional Dual-attentive Networks, which has been submitted to WSDM 2022 for blind review.

We publish a collected dataset (i.e., WiFi-Trace) as a new benchmark. It is available at /data/

Requirements

• python 3.8.2
• numpy 1.18.1
• torch 1.4.0

Hardware Configurations

All experiments are conducted on a server with the following configurations:

• Operating System: CentOS Linux release 7.4.1708
• CPU: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.20GHz
• GPU: GeForce GTX TITAN X

Run the code

python main.py --data_name=foursquare --data_path=../data/