vGraph: A Generative Model For Joint Community Detection and Node Representational Learning
This is a Pytorch implementation of the paper vGraph: A Generative Model For Joint Community Detection and Node Representational Learning and is done under the NeurIPS Reproducibility Challenge 2019. The original implementation by author can be found here.
Summary of the paper
This paper proposes a novel technique for learning node representations and at the same time perform community detection task for the graphical data by creating a generative model using the variational inference concepts. The full paper summary along with its main contributions can be found here
Setup Instructions and Dependancies
The code has been written in Python 3.6 and Pytorch v1.1. Also Pytorch Geometric has been used for training procedures, along with the usage of TensorboardX for logging loss curves.
For training/testing the model, you must first download
Facebook social circles dataset. It can be found here. After downloading the dataset, all the files must be placed inside
The following is the information regarding the various important files in the directory and their functions:
model.py: File containing the network architecture
utils.py: File containing helper functions and losses
data.py: File containing functions to call dataset in an operable format
train_nonoverlapping.py: File containing the training procedure for non-overlapping dataset
train_overlapping.py: File containing the training procedure for overlapping dataset
Running the model
For training the model, use the following commands:
python train_nonoverlapping.py ### For training non-overlapping dataset python train_overlapping.py ### For training overlapping dataset
Current Status of the Project
Currently the directory contains dataloader and training procedure for 2 non-overlapping datasets(
Citeseer) and 10 overlapping datasets(
facebook698). I plan to add more dataloaders in the directory. Also the various accuracy measures as specified in the paper will also soon be added in the repository.
If you found the codebase useful in your research work, consider citing the original paper
This repository is licensed under MIT License