GraphCrop: Subgraph Cropping for Graph Classification
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi

TL;DR
GraphCrop introduces a node-centric subgraph cropping data augmentation technique for GNNs, improving graph classification by promoting global structural understanding and enhancing model generalization.
Contribution
The paper proposes GraphCrop, a simple, parameter-free subgraph cropping method that improves GNN performance by regularizing training and simulating real-world structural noise.
Findings
Significant accuracy improvements on multiple datasets.
Enhanced GNN generalization and robustness.
Easy integration into existing GNN frameworks.
Abstract
We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification. Observing that the omission of sub-structures does not necessarily change the class label of the whole graph, we develop the \textbf{GraphCrop} (Subgraph Cropping) data augmentation method to simulate the real-world noise of sub-structure omission. In principle, GraphCrop utilizes a node-centric strategy to crop a contiguous subgraph from the original graph while maintaining its connectivity. By preserving the valid structure contexts for graph classification, we encourage GNNs to understand the content of graph structures in a global sense, rather than rely on a few key nodes or edges, which may not always be present. GraphCrop is parameter learning free and easy to implement within existing GNN-based graph classifiers. Qualitatively, GraphCrop expands the existing…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Complex Network Analysis Techniques
