# RLINK: Deep Reinforcement Learning for User Identity Linkage

arXiv: https://arxiv.org/abs/1910.14273 · HTML: https://mlsanity.com/paper/1910.14273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

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

- [WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning](https://mlsanity.com/paper/2212.14182/) (2022)
- [Stochastic Intervention for Causal Inference via Reinforcement Learning](https://mlsanity.com/paper/2105.13514/) (2021)
- [Network Alignment](https://mlsanity.com/paper/2504.11367/) (2025)

## Abstract (arXiv)

User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods ignore the results of previously matched identities, which could contribute to the linkage in following matching steps. To address this problem, we convert user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, and explores the long-term influence of current matching on subsequent decisions. We conduct experiments on different types of datasets, the results show that our method achieves better performance than other state-of-the-art methods.