zxj32 / uncertainty-GNN

Uncertainty Aware Semi-Supervised Learning on Graph Data

Uncertainty-GNN

This is a TensorFlow implementation of the uncertainty-GNN model as described in our paper:

Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho. [Uncertainty Aware Semi-Supervised Learning on Graph Data], NeurIPS 2020 (Spotlight)

[slides] [poster]

A multi-source uncertainty framework of GNN that reflecting various types of uncertainties in both deep learning and belief/evidence theory domains for node classification predictions.

Installation

1. Clone this repository.

   git clone https://github.com/zxj32/uncertainty-GNN
   cd uncertainty-GNN

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

• TensorFlow (1.x)
• python 3.6.9
• networkx
• scikit-learn
• scipy
• numpy
• pickle

Run the demo

After set up above 2 steps, you are ready to run our code. You can directly run a demo code:

python S-GCN.py

For our proposed S-BGNN-T-K model, you need to run following code. For example, to get prior Dirichlet Distribution (save in "data/prior/"), please try to run:

python Kernel_Graph.py

to get pretrain Teacher network (save in "data/output/" or "data/ood/"), please try to run:

python Baseline.py -dataset cora --model GCN --OOD_detection 1

to run our method (S-BGNN-T-K) (--OOD_detection: 1 means OOD detection task, 0 means Misclassification task):

python S-BGCN-T-K.py -dataset cora --OOD_detection 1

If there is no error display, then we are done for this section.

(Notice: Our code is based on original GCN code: https://github.com/tkipf/gcn)

Dataset

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)
• an N by E binary label matrix (E is the number of classes).

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

Our paper used six datasets, including

• citation network datasets

Cora, Citeseer, and Pubmed datasets, the original datasets can be found here: http://www.cs.umd.edu/~sen/lbc-proj/LBC.html.

• Coauthor Physics, Amazon Computer, and Amazon Photo

Provided by https://github.com/shchur/gnn-benchmark (Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann, Pitfalls of Graph Neural Network Evaluation, Relational Representation Learning Workshop, NeurIPS 2018).

Models

You can choose between the following models:

Our proposed:

• S-GCN: Subjective GCN that can estimate two evidential uncertainty types (vacuity and dissonance).
• S-BGCN: Subjective GCN with the Bayesian framework that can estimate all four uncertainty types, including aleatoric uncertainty, epistemic uncertainty, vacuity and dissonance.
• S-BGCN-T: S-BGCN with a Teacher network to improve the expected class probability estimation.
• S-BGCN-T-K: S-BGNN-T with Graph kernel Dirichlet Distribution Estimation (GKDE) to improve the uncertainty (Dirichlet distribution) estimation.

basline:

• GCN: Softmax-based GCN with uncertainty measured based on entropy (Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017)
• Drop-GCN: adapts the Monte-Carlo Dropout into the GCN model to learn probabilistic uncertainty (Y. Gal and Z. Ghahramani, Dropout as a bayesian approximation: Representing model uncertainty in deep learning, ICML 2016)
• EDL-GCN: adapts the EDL model with GCN to estimate evidential uncertainty (Murat Sensoy, Lance Kaplan, Melih Kandemir, Evidential Deep Learning to Quantify Classification Uncertainty, NeurIPS 2018)
• DPN-GCN: adapts the DPN [14] method with GCN to estimate probabilistic uncertainty (Andrey Malinin, Mark Gales, Predictive Uncertainty Estimation via Prior Networks, NeurIPS 2018)

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:

@article{zhao2020uncertainty,
  title={Uncertainty Aware Semi-Supervised Learning on Graph Data},
  author={Zhao, Xujiang and Chen, Feng and Hu, Shu and Cho, Jin-Hee},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}