Implementation of 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.
Li Zhang ,Haiping Lu, https://dl.acm.org/doi/abs/10.1145/3340531.3411983 (CIKM 2020)
For official implementation https://github.com/LiZhang-github/LA-GCN/tree/master/code
- PyTorch 0.4 or 0.5
- Python 2.7 or 3.6
python train.py[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
