This is a PyTorch implementation of the paper "Continuous-time Graph Representation with Sequential Survival Process".
- Initialize a new conda environment and activate it.
conda create -n grassp python=3.11
conda activate grassp
- You can install all the required packages by running the following command.
pip install -r requirements.txt
Note : Please visit the PyTorch website for the installation details of the PyTorch library (version Stable 2.0.0 with possible CUDA 11.7 support) regarding your system.
- You can train the model for the Synthetic-mu dataset with CPU processors by typing the following command:
python run.py --edges ../datasets/synthetic_n=100_seed=16/synthetic_n=100_seed=16.edges --model_path ./synthetic_n=100_seed=16.model --device cpu
- To see more detailed list of options, please type:
python run.py --help
- You can generate the animations for the learned embedding by
python animate.py --edges datasets/synthetic_n=100_seed=16/synthetic_n=100_seed=16.edges --model_path synthetic_n=100_seed=16.model --anim_path ynthetic_n=100_seed=16.mp4