Learnable Aggregator for Graph Convolutional Networks in PyTorch

Implementation of Learnable Aggregators for Graph Convolutional Networks in PyTorch.

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 (CIKM 2020)

For official implementation

Requirements

PyTorch 1.8
Python 3.8

Training

python train.py

You can also try out in colab if you don't have any requirements!

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, ICML 2016).

You can specify a dataset as follows:

For Citeseer:

python train.py --dataset "citeseer"

For Cora:

python train.py --dataset "cora"

For Pubmed:

python train.py --dataset "pubmed"

Reference

[1] Zhang & Lu, A Feature-Importance-Aware and Robust Aggregator for GCN, CIKM 2020

[2] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016

Citation

@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}
}

@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}
}