# Deep Active Learning for Anchor User Prediction

arXiv: https://arxiv.org/abs/1906.07318 · HTML: https://mlsanity.com/paper/1906.07318/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors](https://mlsanity.com/paper/2111.11335/) (2021)
- [AvAtar: Learning to Align via Active Optimal Transport](https://mlsanity.com/paper/2605.24395/) (2026)
- [Network Alignment](https://mlsanity.com/paper/2504.11367/) (2025)
- [RANA: Robust Active Learning for Noisy Network Alignment](https://mlsanity.com/paper/2507.22434/) (2025)

## Abstract (arXiv)

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction. To this end, we present a deep active learning model for anchor user prediction (DALAUP for short). However, active learning for anchor user sampling meets the challenges of non-i.i.d. user pair data caused by network structures and the correlation among anchor or non-anchor user pairs. To solve the challenges, DALAUP uses a couple of neural networks with shared-parameter to obtain the vector representations of user pairs, and ensembles three query strategies to select the most informative user pairs for labeling and model training. Experiments on real-world social network data demonstrate that DALAUP outperforms the state-of-the-art approaches.