MetaCas

A PyTorch implementation of our MetaCas.

Dependencies

Install the dependencies via Anaconda:

• Python (>=3.8)
• PyTorch (>=1.8.1)
• NumPy (>=1.17.4)
• Scipy (>=1.7.3)
• torch-geometric(>=2.0.4)
• tqdm(>=4.62.2)

create virtual environment:

conda create --name MetaCas python=3.8

activate environment:

conda activate MetaCas

install pytorch from pytorch:

conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=10.2 -c pytorch

To install all dependencies:

pip install -r requirements.txt

Usage

Here we provide the implementation of MetaCas along with twitter dataset.

• To generate cascade attributes:

python cas_attribute.py

• To train and evaluate on Twitter:

python run.py -data_name=twitter

More running options are described in the codes, e.g., -data_name= twitter

Folder Structure

MetaCas

└── data: # The file includes datasets
    ├── twitter
       ├── cascades.txt       # original data
       ├── cascadetrain.txt   # training set
       ├── cascadevalid.txt   # validation set
       ├── cascadetest.txt    # testing data
       ├── edges.txt          # social network
       ├── idx2u.pickle       # idx to user_id
       ├── u2idx.pickle       # user_id to idx
       
└── models: # The file includes each part of the modules in MetaCas.
    ├── Meta_GNN.py # The core source code of Meta_GNN.
    ├── MetaLSTM.py # The core source code of MetaLSTM.
    ├── TransformerBlock.py # The core source code of time-aware attention.

└── utils: # The file includes each part of basic modules (e.g., metrics, earlystopping).
    ├── EarlyStopping.py  # The core code of the early stopping operation.
    ├── Metrics.py        # The core source code of metrics.
    ├── graphConstruct.py # The core source code of building social network.
    ├── parsers.py        # The core source code of parameter settings. 
└── Constants.py:     
└── cas_attribute.py:  # The file includes the core source code of constructing cascade attributes.
└── dataLoader.py:     # Data loading.
└── run.py:            # Run the model.
└── Optim.py:          # Optimization.