INFLECT

Code for the paper Tiukhova et al. (2023) INFLECT-DGNN: INFLuencer prEdiCTion with Dynamic Graph Neural Networks

Project structure:

The project repo holds the following structure

 |-models
 | |-GNNs.py
 | |-RNNs.py
 | |-decoder.py
 | |-models.py
 |-utils
 | |-utils.py
 |-train.py
 |-requirements.txt
  
 

models

This folder contains the .py files used to make combinations of encoder and decoder in dynamic GNN models as well as create baseline models.

utils

This folder contains a .py file that provides functions for several files.

make_data.py

The script to generate the network data and preprocess it.

train.py

The script to run the experiments.

requirements.txt

The files that lists all of a project's dependencies.