# Control-based Graph Embeddings with Data Augmentation for Contrastive Learning

arXiv: https://arxiv.org/abs/2403.04923 · HTML: https://mlsanity.com/paper/2403.04923/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 7 Mar 2024.

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

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Hypergraph Contrastive Collaborative Filtering](https://mlsanity.com/paper/2204.12200/) (2022)
- [NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics](https://mlsanity.com/paper/2306.06202/) (2023)
- [Controllability Metrics, Limitations and Algorithms for Complex Networks](https://mlsanity.com/paper/1308.1201/) (2013)
- [On Submodularity and Controllability in Complex Dynamical Networks](https://mlsanity.com/paper/1404.7665/) (2014)
- [Zero forcing sets and controllability of dynamical systems defined on graphs](https://mlsanity.com/paper/1403.4868/) (2014)
- [Graph Distances and Controllability of Networks](https://mlsanity.com/paper/1608.04309/) (2016)
- [Controllability Backbone in Networks](https://mlsanity.com/paper/2309.02649/) (2023)
- [Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability](https://mlsanity.com/paper/2003.05490/) (2020)

## Built on by (most cited first, 2 of 2)

- [Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory](https://mlsanity.com/paper/2405.03706/) (2024)
- [Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning](https://mlsanity.com/paper/2507.15195/) (2025)

## Abstract (arXiv)

In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.