# M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification

arXiv: https://arxiv.org/abs/2007.05700 · HTML: https://mlsanity.com/paper/2007.05700/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [Automated Data Augmentations for Graph Classification](https://mlsanity.com/paper/2202.13248/) (2022)
- [Data Augmentation on Graphs: A Technical Survey](https://mlsanity.com/paper/2212.09970/) (2022)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking](https://mlsanity.com/paper/2102.08013/) (2021)
- [Supervised Contrastive Learning with Structure Inference for Graph Classification](https://mlsanity.com/paper/2203.07691/) (2022)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning](https://mlsanity.com/paper/2403.17980/) (2024)
- [RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks](https://mlsanity.com/paper/2108.07481/) (2021)
- [Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study](https://mlsanity.com/paper/2311.12737/) (2023)
- [A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures](https://mlsanity.com/paper/2403.16004/) (2024)
- [Adaptive Substructure-Aware Expert Model for Molecular Property Prediction](https://mlsanity.com/paper/2504.05844/) (2025)

## Abstract (arXiv)

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 in the benchmark datasets makes it easy for graph classification models to fall into over-fitting and undergeneralization. To improve this, we introduce data augmentation on graphs (i.e. graph augmentation) and present four methods:random mapping, vertex-similarity mapping, motif-random mapping and motif-similarity mapping, to generate more weakly labeled data for small-scale benchmark datasets via heuristic transformation of graph structures. Furthermore, we propose a generic model evolution framework, named M-Evolve, which combines graph augmentation, data filtration and model retraining to optimize pre-trained graph classifiers. Experiments on six benchmark datasets demonstrate that the proposed framework helps existing graph classification models alleviate over-fitting and undergeneralization in the training on small-scale benchmark datasets, which successfully yields an average improvement of 3 - 13% accuracy on graph classification tasks.