This repository contains codes of Induced Relational GCN(IR-GCN).
If this code helps you in your research, please cite the following publication:
Ranking User-Generated Content via Multi-Relational Graph Convolution
These instructions will help you setup the proposed model on your local machine.
- Unix, MacOS, Windows (with appropriate Python and PyTorch environment)
Our framework can be compiled on Python 3.6+ environments with the following modules installed:
These requirements may be satisified with an updated Anaconda environment as well - https://www.anaconda.com/
Download stackexchange dataset. In the preprocess folder, run the following command to preprocess the dataset:
$ sh extract.sh <path/to/raw/stackexchange dataset>
The preprocessed dataset will be used as input to the model.
The other parameters to be configured are:
NUM_EPOCH: Number of Epochs for training (Default = 500)
BATCH_SIZE: Size of each batch (Default = 400)
LEARNING_RATE: Learning Rate of the Model (Default = 0.001)
For training and test the model, run the following command:
$ python train.py --dataset <path/to/input/folder>