# Robust Graph Neural Networks via Unbiased Aggregation

arXiv: https://arxiv.org/abs/2311.14934 · HTML: https://mlsanity.com/paper/2311.14934/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 24 of 24)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Graph Random Neural Network for Semi-Supervised Learning on Graphs](https://mlsanity.com/paper/2005.11079/) (2020)
- [Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective](https://mlsanity.com/paper/1906.04214/) (2019)
- [GNNGuard: Defending Graph Neural Networks against Adversarial Attacks](https://mlsanity.com/paper/2006.08149/) (2020)
- [Graph-Coupled Oscillator Networks](https://mlsanity.com/paper/2202.02296/) (2022)
- [A Unified View on Graph Neural Networks as Graph Signal Denoising](https://mlsanity.com/paper/2010.01777/) (2020)
- [Robustness of Graph Neural Networks at Scale](https://mlsanity.com/paper/2110.14038/) (2021)
- [TDGIA:Effective Injection Attacks on Graph Neural Networks](https://mlsanity.com/paper/2106.06663/) (2021)
- [Elastic Graph Neural Networks](https://mlsanity.com/paper/2107.06996/) (2021)
- [Certifiable Robustness to Graph Perturbations](https://mlsanity.com/paper/1910.14356/) (2019)
- [Graph Neural Networks Inspired by Classical Iterative Algorithms](https://mlsanity.com/paper/2103.06064/) (2021)
- [Reliable Graph Neural Networks via Robust Aggregation](https://mlsanity.com/paper/2010.15651/) (2020)
- [Are Defenses for Graph Neural Networks Robust?](https://mlsanity.com/paper/2301.13694/) (2023)
- [Batch Virtual Adversarial Training for Graph Convolutional Networks](https://mlsanity.com/paper/1902.09192/) (2019)
- [EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks](https://mlsanity.com/paper/2205.13892/) (2022)
- [Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach](https://mlsanity.com/paper/2310.06396/) (2023)
- [GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks](https://mlsanity.com/paper/2201.12741/) (2022)
- [Vector-Valued Graph Trend Filtering with Non-Convex Penalties](https://mlsanity.com/paper/1905.12692/) (2019)
- [Total Variation Graph Neural Networks](https://mlsanity.com/paper/2211.06218/) (2022)

## Built on by (most cited first, 8 of 8)

- [Boosting Adversarial Robustness and Generalization with Dictionary Structure](https://mlsanity.com/paper/2502.00834/) (2025)
- [Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses](https://mlsanity.com/paper/2510.17185/) (2025)
- [Domain Adaptive Unfolded Graph Neural Networks](https://mlsanity.com/paper/2411.13137/) (2024)
- [Adaptive Branch Specialization in Spectral-Spatial Graph Neural Networks for Certified Robustness](https://mlsanity.com/paper/2505.08320/) (2025)
- [Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs](https://mlsanity.com/paper/2603.21155/) (2026)
- [Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation](https://mlsanity.com/paper/2605.05534/) (2026)
- [GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks](https://mlsanity.com/paper/2606.01560/) (2026)
- [Towards One-for-All Robustness Across a Continuum of Threat Levels](https://mlsanity.com/paper/2609.02440/) (2026)

## Abstract (arXiv)

The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses. In this work, we delve into the robustness analysis of representative robust GNNs and provide a unified robust estimation point of view to understand their robustness and limitations. Our novel analysis of estimation bias motivates the design of a robust and unbiased graph signal estimator. We then develop an efficient Quasi-Newton Iterative Reweighted Least Squares algorithm to solve the estimation problem, which is unfolded as robust unbiased aggregation layers in GNNs with theoretical guarantees. Our comprehensive experiments confirm the strong robustness of our proposed model under various scenarios, and the ablation study provides a deep understanding of its advantages. Our code is available at https://github.com/chris-hzc/RUNG.