# GraphVICRegHSIC: Towards improved self-supervised representation learning for graphs with a hyrbid loss function

arXiv: https://arxiv.org/abs/2105.12247 · HTML: https://mlsanity.com/paper/2105.12247/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 20 of 20)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [Graph Representation Learning via Graphical Mutual Information Maximization](https://mlsanity.com/paper/2002.01169/) (2020)
- [Self-supervised Pretraining of Visual Features in the Wild](https://mlsanity.com/paper/2103.01988/) (2021)
- [Self-Supervised Learning of Graph Neural Networks: A Unified Review](https://mlsanity.com/paper/2102.10757/) (2021)
- [A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning](https://mlsanity.com/paper/2104.13712/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction](https://mlsanity.com/paper/2502.10776/) (2025)

## Abstract (arXiv)

Self-supervised learning and pre-training strategieshave developed over the last few years especiallyfor Convolutional Neural Networks (CNNs). Re-cently application of such methods can also be no-ticed for Graph Neural Networks (GNNs) . In thispaper, we have used a graph based self-supervisedlearning strategy with different loss functions (Bar-low Twins[Zbontaret al., 2021], HSIC[Tsaiet al.,2021], VICReg[Bardeset al., 2021]) which haveshown promising results when applied with CNNspreviously. We have also proposed a hybrid lossfunction combining the advantages of VICReg andHSIC and called it as VICRegHSIC. The perfor-mance of these aforementioned methods have beencompared when applied to 7 different datasets suchas MUTAG, PROTEINS, IMDB-Binary, etc. Ex-periments showed that our hybrid loss function per-formed better than the remaining ones in 4 out of7 cases. Moreover, the impact of different batchsizes, projector dimensions and data augmentationstrategies have also been explored.