RLINK: Deep Reinforcement Learning for User Identity Linkage

TL;DR

This paper introduces RLINK, a reinforcement learning approach that improves user identity linkage across social networks by considering global matching strategies and historical matches, outperforming existing methods.

Contribution

The paper proposes a novel reinforcement learning framework for user identity linkage that leverages social network structure and historical matches for improved accuracy.

Findings

RLINK outperforms state-of-the-art methods on various datasets.

The method effectively utilizes long-term influence of matching decisions.

Experimental results demonstrate significant performance gains.

Abstract

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.