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arXiv · 2009.09863

Data Augmentation for Graph Classification

Jiajun Zhou·Jie Shen·Qi Xuan
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Abstract

Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the limitation of scale of benchmark datasets makes it easy for graph classification models to fall into over-fitting and undergeneralization. Towards this, we introduce data augmentation on graphs and present two heuristic algorithms: random mapping and motif-similarity mapping, to generate more weakly labeled data for small-scale benchmark datasets via heuristic modification of graph structures. Furthermore, we propose a generic model evolution framework, M-Evolve, which combines graph augmentation, data filtration and model retraining to optimize pre-trained graph classifiers. Experiments conducted on six benchmark datasets demonstrate that M-Evolve helps existing graph classification models alleviate over-fitting when training on small-scale benchmark datasets and yields an average improvement of 3-12% accuracy on graph classification tasks.

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Jiajun Zhou, Jie Shen, Qi Xuan. 2020-09-18. Data Augmentation for Graph Classification. https://doi.org/10.1145/3340531.3412086

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