GCN-KL

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

Xujiang Zhao, Feng Chen, Jin-Hee Cho Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks, MILCOM (2018)

GCN-KL model are end-to-end trainable neural network models for uncertain opinions prediction in a network data..

Installation

Clone this repository.

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

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 opinion_KL.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_load_data_traffic function in traffic_data/read_data.py for an example.

In this example, we load traffic data. The original datasets can be found here: http://inrix.com/publicsector.asp

Models

You can choose the following model:

GCN-KL: opinion_KL.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{zhao2018uncertainty,
  title={Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks},
  author={Zhao, Xujiang and Chen, Feng and Cho, Jin-Hee},
  booktitle={MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM)},
  pages={731--736},
  year={2018},
  organization={IEEE}
}