GCN-GRU

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

Xujiang Zhao, Feng Chen, Jin-Hee Cho [Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data], Bigdata (2018)

GCN-GRU model are end-to-end trainable deep learning models for dynamic uncertain opinions prediction in dynamic network data.

Installation

1. Clone this repository.

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

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_GRU_run.py

If your graph is very large, please use

python GCN_GRU_sparse.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 L by T ground truth matrix (L is the dimension of label per node, T is time length)
• an N by D by T feature matrix (D is the number of features per node, T is time length) -- optional (if you don't have feature, our model will use the indentity matrix as default feature)

Have a look at the generate_train_test_epinion() or generate_train_test_epinion_sparse() function in read_data/read_data.py for an example.

In this example, we load epinion data with a subgraph 500 nodes. The original datasets can be found here:http://www.trustlet.org/downloaded

Models

For now, you can only choose the following model:

• GCN-GRU: GCN_GRU_run.py
• GCN-GRU (sparse): GCN_GRU_sparse.py

I will upload the other baseline models later.

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 for Predicting Dynamic Uncertain Opinions in Network Data},
  author={Zhao, Xujiang and Chen, Feng and Cho, Jin-Hee},
  booktitle={2018 IEEE International Conference on Big Data (Big Data)},
  pages={1150--1155},
  year={2018},
  organization={IEEE}
}