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https://github.com/sk1712/gcn_metric_learning
Metric Learning with Graph Convolutional Neural Networks
https://github.com/sk1712/gcn_metric_learning
Last synced: about 2 months ago
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Metric Learning with Graph Convolutional Neural Networks
- Host: GitHub
- URL: https://github.com/sk1712/gcn_metric_learning
- Owner: sk1712
- License: mit
- Created: 2017-05-26T16:44:40.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-10-14T14:15:41.000Z (almost 6 years ago)
- Last Synced: 2024-07-29T07:32:14.523Z (about 2 months ago)
- Language: Python
- Size: 23.4 KB
- Stars: 200
- Watchers: 9
- Forks: 71
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Metric Learning using Graph Convolutional Neural Networks (GCNs)
The code in this repository implements a metric learning approach for irregular
graphs. The method has been applied on brain connectivity networks and is
presented in our papers:
* Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee,
Ben Glocker, Daniel Rueckert, [Metric learning with spectral graph convolutions on brain connectivity networks](https://www.sciencedirect.com/science/article/pii/S1053811917310765), NeuroImage, 2018.
* Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee,
Ben Glocker, Daniel Rueckert, [Distance Metric Learning using Graph Convolutional
Networks: Application to Functional Brain Networks](https://arxiv.org/abs/1703.02161), Medical Image Computing
and Computer-Assisted Interventions (MICCAI), 2017.
The code is released under the terms of the [MIT license](LICENSE.txt). Please
cite the above paper if you use it.
There is also implementations of the filters and graph coarsening used in:
* Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, [Convolutional Neural
Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), Neural
Information Processing Systems (NIPS), 2016.
The implementaton of the global loss function is based on:
* Vijay Kuma, Gustavo Carneiro, Ian Reid, [Learning Local Image Descriptors with Deep
Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions](https://arxiv.org/abs/1512.09272),
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
## Installation
1. Clone this repository.
```sh
git clone https://github.com/sk1712/gcn_metric_learning
cd gcn_metric_learning
```
2. Install the dependencies. Please edit `requirements.txt` to choose the
TensorFlow version (CPU / GPU, Linux / Mac) you want to install, or install
it beforehand.
```sh
pip install -r requirements.txt # or make install
```
## Using the model
To use our siamese graph ConvNet on your data, you need:
1. pairs of graphs as matrices where each row is a node and each column is a node feature,
2. a class label for each graph,
3. an adjacency matrix which provides the structure as a graph; the same structure
will be used for all samples.
Please get in touch if you are unsure about applying the model to a different
setting.