# RANA: Robust Active Learning for Noisy Network Alignment

arXiv: https://arxiv.org/abs/2507.22434 · HTML: https://mlsanity.com/paper/2507.22434/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Active Learning for Graph Embedding](https://mlsanity.com/paper/1705.05085/) (2017)
- [Robust Graph Learning from Noisy Data](https://mlsanity.com/paper/1812.06673/) (2018)
- [RIM: Reliable Influence-based Active Learning on Graphs](https://mlsanity.com/paper/2110.14854/) (2021)
- [Information Gain Propagation: a new way to Graph Active Learning with Soft Labels](https://mlsanity.com/paper/2203.01093/) (2022)
- [Uncertainty for Active Learning on Graphs](https://mlsanity.com/paper/2405.01462/) (2024)
- [SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs](https://mlsanity.com/paper/1908.08169/) (2019)
- [Deep Active Learning for Anchor User Prediction](https://mlsanity.com/paper/1906.07318/) (2019)
- [Active Network Alignment: A Matching-Based Approach](https://mlsanity.com/paper/1610.05516/) (2016)
- [Robust Offline Active Learning on Graphs](https://mlsanity.com/paper/2408.07941/) (2024)
- [User Identity Linkage on Social Networks: A Review of Modern Techniques and Applications](https://mlsanity.com/paper/2409.08966/) (2024)

## Abstract (arXiv)

Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. RANA effectively tackles both structure noise and label noise while addressing the sparsity of anchor link annotations, which can improve the robustness of network alignment models. Specifically, RANA introduces the proposed Noise-aware Selection Module and the Label Denoising Module to address structural noise and labeling noise, respectively. In the first module, we design a noise-aware maximization objective to select node pairs, incorporating a cleanliness score to address structural noise. In the second module, we propose a novel multi-source fusion denoising strategy that leverages model and twin node pairs labeling to provide more accurate labels for node pairs. Empirical results on three real-world datasets demonstrate that RANA outperforms state-of-the-art active learning-based methods in alignment accuracy. Our code is available at https://github.com/YXNan0110/RANA.