Data Augmentation for Graph Neural Networks (2006.06830v2)

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.

Data Augmentation for Graph Neural Networks

The paper "Data Augmentation for Graph Neural Networks" addresses the challenge of improving the performance of Graph Neural Networks (GNNs) through data augmentation techniques. While data augmentation has been extensively utilized in other machine learning domains such as computer vision and natural language processing, its application to graphs has been relatively unexplored due to the non-Euclidean nature of graph data. The paper introduces a novel framework, GAug, which leverages edge prediction to manipulate graph structures, aiming to enhance semi-supervised node classification.

Key Contributions

1. Graph Augmentation via Edge Manipulation: The authors propose modifying the graph by adding or removing edges to create a modified graph, which can be especially beneficial for node classification tasks. They highlight two augmentation settings: the modified-graph setting (GAug-M) and the original-graph setting (GAug-O), contingent on whether graph manipulation at inference time is feasible.
2. Neural Edge Predictors: The paper introduces the idea of using neural edge predictors like the Graph Auto-Encoder (GAE) to intelligently determine which edges to add or subtract to approximate class-homophilic tendencies. This strategy is shown to increase intra-class connectivity and reduce inter-class edges, thereby enhancing the GNN's ability to generalize.
3. Empirical Validation: Extensive experiments demonstrate that GAug consistently improves performance across different GNN architectures and datasets, with GAug-O and GAug-M providing up to 9% and 17% increases in F1 score respectively, compared to baseline approaches.

Implications and Future Prospects

The findings suggest several implications for the field of graph machine learning. The proposed augmentation framework significantly aids GNNs under low supervision conditions by enhancing their robustness to noisy graph data, a feature particularly valuable in real-world scenarios involving incomplete or noisy datasets. From a practical standpoint, this framework can be readily integrated into existing GNN architectures without requiring changes to the model's core algorithm.

Theoretically, these results underscore the potential for neural network models to learn structural information from graph data, achieving class distinction through strategic edge manipulations. Such insights could inform further studies on using data augmentation as a means to address other graph-related challenges, like dynamic graph representation learning or temporal graphs where graph structures evolve over time.

Moving forward, the exploration of differentiable edge prediction strategies, beyond GAE, holds promise. This could lead to augmentation strategies that are more adaptable, finding broader applicability across domains where graph structures vary widely. Additionally, the methods outlined in this paper could inspire future work examining the effect of graph augmentation on deeper or more complex GNN architectures, as well as investigating its synergistic potential with other regularization techniques.

In summary, this paper makes a substantial contribution to the graph machine learning community by introducing a robust framework for graph data augmentation that enhances GNN performance through thoughtful manipulation of graph structures. The findings present opportunities for future advancements in both the theoretical development and practical application of GNNs in noisy and limited-data environments.