# Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs

arXiv: https://arxiv.org/abs/2406.03097 · HTML: https://mlsanity.com/paper/2406.03097/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 30 of 31)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Graph Random Neural Network for Semi-Supervised Learning on Graphs](https://mlsanity.com/paper/2005.11079/) (2020)
- [Bayesian Graph Neural Networks with Adaptive Connection Sampling](https://mlsanity.com/paper/2006.04064/) (2020)
- [Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation](https://mlsanity.com/paper/2008.10715/) (2020)
- [N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification](https://mlsanity.com/paper/1802.08888/) (2018)
- [Random Walks: A Review of Algorithms and Applications](https://mlsanity.com/paper/2008.03639/) (2020)
- [Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach](https://mlsanity.com/paper/2310.06396/) (2023)
- [Topological Insights into Sparse Neural Networks](https://mlsanity.com/paper/2006.14085/) (2020)
- [On a Sparse Shortcut Topology of Artificial Neural Networks](https://mlsanity.com/paper/1811.09003/) (2018)
- [Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression](https://mlsanity.com/paper/2306.00788/) (2023)
- [Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision](https://mlsanity.com/paper/2203.03762/) (2022)
- [BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning](https://mlsanity.com/paper/2305.18377/) (2023)
- [RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation](https://mlsanity.com/paper/2205.14005/) (2022)
- [Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation](https://mlsanity.com/paper/2208.09779/) (2022)
- [Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity](https://mlsanity.com/paper/2208.12422/) (2022)
- [Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization](https://mlsanity.com/paper/2305.00374/) (2023)
- [Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs](https://mlsanity.com/paper/2307.11629/) (2023)
- [NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels](https://mlsanity.com/paper/2105.14676/) (2021)
- [Deperturbation of Online Social Networks via Bayesian Label Transition](https://mlsanity.com/paper/2010.14121/) (2020)
- [Uncertainty-Aware Robust Learning on Noisy Graphs](https://mlsanity.com/paper/2306.08210/) (2023)
- [Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis](https://mlsanity.com/paper/2312.12934/) (2023)
- [Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs](https://mlsanity.com/paper/2201.08227/) (2022)
- [Robust Node Representation Learning via Graph Variational Diffusion Networks](https://mlsanity.com/paper/2312.10903/) (2023)
- [LabelRank: A Stabilized Label Propagation Algorithm for Community Detection in Networks](https://mlsanity.com/paper/1303.0868/) (2013)
- [Multipath Graph Convolutional Neural Networks](https://mlsanity.com/paper/2105.01510/) (2021)

## Built on by (most cited first, 3 of 3)

- [RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling](https://mlsanity.com/paper/2408.06665/) (2024)
- [REGE: A Method for Incorporating Uncertainty in Graph Embeddings](https://mlsanity.com/paper/2412.05735/) (2024)
- [Feature Space Topology Control via Hopkins Loss](https://mlsanity.com/paper/2509.11154/) (2025)

## Abstract (arXiv)

Graph neural networks (GNNs) have been extensively employed in node classification. Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations, such as adversarial attacks and edge disruptions. Considerable efforts have been devoted to mitigating these challenges. For example, pioneering Bayesian methodologies, including GraphSS and LlnDT, incorporate Bayesian label transitions and topology-based label sampling to strengthen the robustness of GNNs. However, GraphSS is hindered by slow convergence, while LlnDT faces challenges in sparse graphs. To overcome these limitations, we propose a novel label inference framework, TraTopo, which combines topology-driven label propagation, Bayesian label transitions, and link analysis via random walks. TraTopo significantly surpasses its predecessors on sparse graphs by utilizing random walk sampling, specifically targeting isolated nodes for link prediction, thus enhancing its effectiveness in topological sampling contexts. Additionally, TraTopo employs a shortest-path strategy to refine link prediction, thereby reducing predictive overhead and improving label inference accuracy. Empirical evaluations highlight TraTopo's superiority in node classification, significantly exceeding contemporary GCN models in accuracy.