# Improving Subgraph Representation Learning via Multi-View Augmentation

arXiv: https://arxiv.org/abs/2205.13038 · HTML: https://mlsanity.com/paper/2205.13038/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Graph Contrastive Learning Automated](https://mlsanity.com/paper/2106.07594/) (2021)
- [Data Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2006.06830/) (2020)
- [Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning](https://mlsanity.com/paper/2010.16103/) (2020)
- [Data Augmentation for Deep Graph Learning: A Survey](https://mlsanity.com/paper/2202.08235/) (2022)
- [Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning](https://mlsanity.com/paper/2009.10273/) (2020)
- [Subgraph Neural Networks](https://mlsanity.com/paper/2006.10538/) (2020)
- [Multi-View Graph Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2005.13607/) (2020)

## Built on by (most cited first, 4 of 4)

- [SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning](https://mlsanity.com/paper/2303.03379/) (2023)
- [Stochastic Subgraph Neighborhood Pooling for Subgraph Classification](https://mlsanity.com/paper/2304.08556/) (2023)
- [SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics](https://mlsanity.com/paper/2506.16602/) (2025)
- [Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis](https://mlsanity.com/paper/2510.08655/) (2025)

## Abstract (arXiv)

Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-property relationships and collective cellular function. In particular, graph augmentation techniques have shown promising results in improving graph-based and node-based classification tasks. Still, they have rarely been explored in the existing GNN-based subgraph representation learning studies. In this study, we develop a novel multi-view augmentation mechanism to improve subgraph representation learning models and thus the accuracy of downstream prediction tasks. Our augmentation technique creates multiple variants of subgraphs and embeds these variants into the original graph to achieve highly improved training efficiency, scalability, and accuracy. Benchmark experiments on several real-world biological and physiological datasets demonstrate the superiority of our proposed multi-view augmentation techniques in subgraph representation learning.