ctdg

A Continuous-Time Dynamic Graphs library

Installation

Before installing the library, you need to install some dependencies that are not specified in the pyproject.toml file, so they will not be installed automatically by pip. In particular, you need to install torch and torch-scatter. If you need to use the HDBSCAN-based rewirers of TIPAR and CONNECT, you also need to install the cuml library. Note that the cuml library depends on cupy that requires a CUDA installation on your machine, thus we recommend using a conda environment to install it as it will install all the necessary dependencies for you.

conda create -n ctdg python=3.10

conda install -c rapidsai -c conda-forge -c nvidia cuml=24.08 'cuda-version>=12.0,<=12.5'
# then install torch and torch-scatter

Currently, we do not provide a PyPI package for this library, but you can easily install it from this repository using pip:

pip install git+https://github.com/FrancescoGentile/ctdg.git

If you instead want to modify the library, you can clone the repository and install it in editable mode:

git clone https://github.com/FrancescoGentile/ctdg.git
cd ctdg
pip install -e .

Usage

Other than using the library as a dependency in your project, you can also use it a command-line tool to train and evaluate the provided models on a bunch of different tasks. At the moment, the library supports the following tasks:

• future link prediction: predict the presence of an interaction between two nodes at a given time in the future. The datasets that can be used for this task are the following: Wikipedia, Reddit, MOOC, LASTFM (of course, you can also use your own dataset).
• macroscopic cascade prediction: predict the number of nodes that will be activated by a cascade process. The datasets that can be used for this task are the following: Christianity, Android, Douban, Memetracker, Twitter.
• microscopic cascade prediction: predict the next node that will be activated by a cascade process. The same datasets used for the macroscopic cascade prediction task can be used for this task as well.

The models currently available in the library are the following:

• TGN
• TIGE (this is TIGER without restarts)
• TIPAR
• CONNECT

All implemented models can be trained on the tasks mentioned above. To train a model on a task, you can use the following command:

ctdg {model_name} --phase train --task {task_name} --config {config_file}

where {model_name} is the lowercase name of the model you want to train, {task_name} should one of "link-prediction", "cascade-prediction" (to specify which type of cascade prediction task you want to train the model on, pass a --mode argument with the value "macro" or "micro"), and {config_file} is the path to a configuration file that specifies the dataset to use, the structure of the model and other hyperparameters. As configuration files, we take inspiration from detectron2 and we use python-based configuration files. You can find some examples in the configs directory.