Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
@inproceedings{Godwin2021SimpleGR, title={Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond}, author={Jonathan Godwin and Michael Schaarschmidt and Alex Gaunt and Alvaro Sanchez-Gonzalez and Yulia Rubanova and Petar Velivckovi'c and James Kirkpatrick and Peter W. Battaglia}, booktitle={International Conference on Learning Representations}, year={2021} }
In this paper we show that simple noisy regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both penalise node latent similarity and encourage meaningful node representations. From this observation we derive “Noisy Nodes”, a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss. The diverse node level loss encourages latent node diversity, and the denoising…
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References
SHOWING 1-10 OF 73 REFERENCES
PairNorm: Tackling Oversmoothing in GNNs
- Computer ScienceICLR
- 2020
PairNorm is a novel normalization layer that is based on a careful analysis of the graph convolution operator, which prevents all node embeddings from becoming too similar and significantly boosts performance for a new problem setting that benefits from deeper GNNs.
Revisiting "Over-smoothing" in Deep GCNs
- Computer ScienceArXiv
- 2020
This work interprets a standard GCN architecture as layerwise integration of a Multi-layer Perceptron (MLP) and graph regularization and concludes that before training, the final representation of a deep GCN does over-smooth, however, it learns anti-oversmoothing during training.
Effective Training Strategies for Deep Graph Neural Networks
- Computer ScienceArXiv
- 2020
The proposed NodeNorm regularizes deep GCNs by discouraging feature-wise correlation of hidden embeddings and increasing model smoothness with respect to input node features, and thus effectively reduces overfitting, enabling deep GNNs to compete with and even outperform shallow ones.
Relational Pooling for Graph Representations
- Computer ScienceICML
- 2019
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions to provide a framework with maximal representation power for graphs.
DeeperGCN: All You Need to Train Deeper GCNs
- Computer ScienceArXiv
- 2020
Extensive experiments on Open Graph Benchmark show DeeperGCN significantly boosts performance over the state-of-the-art on the large scale graph learning tasks of node property prediction and graph property prediction.
A Note on Over-Smoothing for Graph Neural Networks
- Computer ScienceArXiv
- 2020
It is shown that when the weight matrix satisfies the conditions determined by the spectrum of augmented normalized Laplacian, the Dirichlet energy of embeddings will converge to zero, resulting in the loss of discriminative power.
Neural Message Passing for Quantum Chemistry
- Computer ScienceICML
- 2017
Using MPNNs, state of the art results on an important molecular property prediction benchmark are demonstrated and it is believed future work should focus on datasets with larger molecules or more accurate ground truth labels.
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
- Computer ScienceAAAI
- 2018
It is shown that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers.
FLAG: Adversarial Data Augmentation for Graph Neural Networks
- Computer ScienceArXiv
- 2020
This work proposes a simple but effective solution, FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training, and boosts performance at test time.
Graph Contrastive Learning with Augmentations
- Computer ScienceNeurIPS
- 2020
The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, the GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods.