Data Augmentation for Graph Neural Networks
- URL: http://arxiv.org/abs/2006.06830v2
- Date: Wed, 2 Dec 2020 17:58:12 GMT
- Title: Data Augmentation for Graph Neural Networks
- Authors: Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang,
Neil Shah
- Abstract summary: We study graph data augmentation for graph neural networks (GNNs) in the context of improving semi-supervised node-classification.
Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure.
Our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction.
- Score: 32.24311481878144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data augmentation has been widely used to improve generalizability of machine
learning models. However, comparatively little work studies data augmentation
for graphs. This is largely due to the complex, non-Euclidean structure of
graphs, which limits possible manipulation operations. Augmentation operations
commonly used in vision and language have no analogs for graphs. Our work
studies graph data augmentation for graph neural networks (GNNs) in the context
of improving semi-supervised node-classification. We discuss practical and
theoretical motivations, considerations and strategies for graph data
augmentation. Our work shows that neural edge predictors can effectively encode
class-homophilic structure to promote intra-class edges and demote inter-class
edges in given graph structure, and our main contribution introduces the GAug
graph data augmentation framework, which leverages these insights to improve
performance in GNN-based node classification via edge prediction. Extensive
experiments on multiple benchmarks show that augmentation via GAug improves
performance across GNN architectures and datasets.
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