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README.md

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 # vGraph: A Generative Model For Joint Community Detection and Node Representational Learning
 
-This is a Pytorch implementation of the paper [*vGraph*](https://arxiv.org/abs/1906.07159) using the Pytorch Geometric and done under the NeurIPS Reproducibility Challenge 2019.
+This is a Pytorch implementation of the paper [vGraph: A Generative Model For Joint Community Detection and Node Representational Learning](https://arxiv.org/abs/1906.07159) and is done under the **NeurIPS Reproducibility Challenge 2019**. The original implementation by author can be found [here](https://github.com/fanyun-sun/vGraph).
+
+## Summary of the paper
+
+<img src='https://github.com/aniket-agarwal1999/vGraph-Pytorch/blob/master/images/model.png'>
+
+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](https://github.com/vlgiitr/papers_we_read/blob/master/summaries/vgraph.md)**
 
 ## 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](https://snap.stanford.edu/data/ego-Facebook.html). After downloading the dataset, all the files must be placed inside `./dataset/Facebook/`.
+
+## Repository Overview
+
+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(`Cora` and `Citeseer`) and 10 overlapping datasets(`facebook0`, `facebook107`, `facebook1684`, `facebook1912`, `facebook3437`, `facebook348`, `facebook3980`, `facebook414`, `facebook686`, `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
+```
+
+## License
+
+This repository is licensed under MIT License