# An Introduction to Robust Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2103.14807 · HTML: https://mlsanity.com/paper/2103.14807/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 27 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Masking: A New Perspective of Noisy Supervision](https://mlsanity.com/paper/1805.08193/) (2018)
- [Object Detection from Video Tubelets with Convolutional Neural Networks](https://mlsanity.com/paper/1604.04053/) (2016)
- [Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network](https://mlsanity.com/paper/1705.00727/) (2017)
- [Robust Convolutional Neural Networks under Adversarial Noise](https://mlsanity.com/paper/1511.06306/) (2015)
- [Learning with Noisy Labels for Sentence-level Sentiment Classification](https://mlsanity.com/paper/1909.00124/) (2019)
- [Learning Robust Representations of Text](https://mlsanity.com/paper/1609.06082/) (2016)
- [Error-Robust Multi-View Clustering](https://mlsanity.com/paper/1801.00384/) (2018)

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

- [Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation](https://mlsanity.com/paper/2405.08645/) (2024)

## Abstract (arXiv)

Graph convolutional neural networks (GCNs) generalize tradition convolutional neural networks (CNNs) from low-dimensional regular graphs (e.g., image) to high dimensional irregular graphs (e.g., text documents on word embeddings). Due to inevitable faulty data collection instruments, deceptive data manipulation, or other system errors, the data might be error-contaminated. Even a small amount of error such as noise can compromise the ability of GCNs and render them inadmissible to a large extent. The key challenge is how to effectively and efficiently employ GCNs in the presence of erroneous data. In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources. By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly. Experimental results on various real-world datasets demonstrate the superiority of the proposed model over the baseline methods and its robustness against different types of error.