# Deep Active Learning for Anchor User Prediction

## TL;DR

This paper introduces DALAUP, a deep active learning model designed to efficiently predict anchor user pairs across social networks by reducing labeling costs and addressing data correlation challenges.

## Contribution

The paper proposes a novel deep active learning framework with neural networks and ensemble query strategies for anchor user prediction across networks.

## Key findings

- DALAUP outperforms existing methods on real-world data.
- The model effectively handles non-i.i.d. data and network structure challenges.
- Ensemble query strategies improve sample selection for labeling.

## Abstract

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.