GCN-VAE

This is a TensorFlow implementation of the GCN-VAE-based model as described in our paper:

Xujiang Zhao, Feng Chen, Jin-Hee Cho [Deep Learning based Scalable Inference of Uncertain Opinions], ICDM (2018)

GCN-VAE-based model are end-to-end trainable neural network models for uncertain opinions prediction in a large-scale network data..

Installation

1. Clone this repository.

   git clone https://github.com/zxj32/GCN-VAE-opinion
   cd GCN-VAE-opinion

2. Install the dependencies. The code should run with TensorFlow 1.0 and newer.

   pip install -r requirements.txt  # or make install

Requirements

• TensorFlow (1.0 or later)
• python 2.7
• networkx
• scikit-learn
• scipy

Run the demo

python gcn_vae_epinion.py

Data

In order to use your own data, you have to provide

• an N by N adjacency matrix (N is the number of nodes), and
• an N by D feature matrix (D is the number of features per node) -- optional

Have a look at the load_data() function in input_data.py for an example.

In this example, we load epinioin data. The original datasets can be found here (epinion data): http://www.trustlet.org/downloaded and here (traffic data): http://inrix.com/publicsector.asp

Models

You can choose between the following models:

• GCN-based: gcn_vae_epinion.py
• GCN-VAE-based: gcn_epinion.py

Question

If you have any question, please feel free to contact me. Email is good for me.

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{zhao2018deep,
  title={Deep Learning Based Scalable Inference of Uncertain Opinions},
  author={Zhao, Xujiang and Chen, Feng and Cho, Jin-Hee},
  booktitle={2018 IEEE International Conference on Data Mining (ICDM)},
  pages={807--816},
  year={2018},
  organization={IEEE}
}