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Implementation of Learnable Aggregator for Graph Convolutional Networks in PyTorch

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# Learnable Aggregator for Graph Convolutional Networks in PyTorch

Implementation of Learnable Aggregators for Graph Convolutional Networks in PyTorch.

![LA-GCN with Mask Aggregator](https://github.com/asarigun/la-gcn-torch/blob/main/images/model.jpg)

Learnable Aggregator for GCN (LA-GCN) by introducing a shared auxiliary model that provides a
customized schema in neighborhood aggregation. Under this framework, a new model proposed called
LA-GCN(Mask) consisting of a new aggregator function, mask aggregator. The auxiliary model
learns a specific mask for each neighbor of a given node, allowing both node-level and feature-level 
attention. This mechanism learns to assign different importance to both nodes and features for prediction, 
which provides interpretable explanations for prediction and increases the model robustness. [1]

Li  Zhang ,Haiping  Lu, [A Feature-Importance-Aware and Robust Aggregator for GCN](https://dl.acm.org/doi/abs/10.1145/3340531.3411983) (CIKM 2020)

For official implementation  [![report](https://img.shields.io/badge/Official-Code-yellow)](https://github.com/LiZhang-github/LA-GCN/tree/master/code)

## Requirements

* PyTorch 1.8
  * Python 3.8
  
  
## Training

```bash
python train.py
```

You can also try out in colab if you don't have any requirements! <a href="https://colab.research.google.com/drive/1BurTEjf6sqtIfnVn9FdCGxBwiCCHiNAN?usp=sharing" alt="license"><img src="https://colab.research.google.com/assets/colab-badge.svg"/></a>

Note: Since random inits, your training results may not exact the same as reported in the paper!

## Data

In order to use your own data, you have to provide 
* an N by N adjacency matrix (N is the number of nodes), 
* an N by D feature matrix (D is the number of features per node), and
* an N by E binary label matrix (E is the number of classes). [2]

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

In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://www.cs.umd.edu/~sen/lbc-proj/LBC.html. In our version (see `data` folder) we use dataset splits provided by https://github.com/kimiyoung/planetoid (Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov, [Revisiting Semi-Supervised Learning with Graph Embeddings](https://arxiv.org/abs/1603.08861), ICML 2016).

You can specify a dataset as follows:

* For Citeseer: 
```bash
python train.py --dataset "citeseer"
```
* For Cora: 
```bash
python train.py --dataset "cora"
```
* For Pubmed: 
```bash
python train.py --dataset "pubmed"
```

## Reference

[1] [Zhang & Lu, A Feature-Importance-Aware and Robust Aggregator for GCN, CIKM 2020](https://dl.acm.org/doi/abs/10.1145/3340531.3411983) [![report](https://img.shields.io/badge/Official-Code-yellow)](https://github.com/LiZhang-github/LA-GCN/tree/master/code)

[2] [Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016](https://arxiv.org/abs/1609.02907) [![report](https://img.shields.io/badge/Official-Code-ff69b4)](https://github.com/tkipf/pygcn)

## Citation

```bibtex
@inproceedings{zhang2020feature,
  title={A Feature-Importance-Aware and Robust Aggregator for GCN},
  author={Zhang, Li and Lu, Haiping},
  booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
  pages={1813--1822},
  year={2020}
}
```
```bibtex
@article{kipf2016semi,
  title={Semi-supervised classification with graph convolutional networks},
  author={Kipf, Thomas N and Welling, Max},
  journal={arXiv preprint arXiv:1609.02907},
  year={2016}
}
```

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