High-Order Consistency-Guided User Identity Linkage with Large Language Model

Tianpeng Li, Xiangjun Pei, Yinghui Wang, Qiyao Peng, Wenjun Wang

Abstract: With the rapid expansion of the Internet, people commonly maintain multiple account identities across different online platforms, creating latent cross-network associations. User Identity Linkage (UIL), which seeks to identify and associate multiple accounts belonging to the same individual across platforms, has emerged as a vital research direction with broad applications in cross-platform recommendation, unified user profiling, and so on. However, existing methods face two major challenges in real-world environments: cross-platform feature heterogeneity and attribute-structure representation fusion. To address these challenges, this paper propose a Multi-View Feature High-Order Consistency-Guided User Identity Linkage method UIL-HC-MV. Our approach mitigates cross-network heterogeneity by deeply integrating multi-view features and mining consistency in shared thematic information among users and their relational networks. We decompose cross-platform feature heterogeneity into two subproblems: attribute heterogeneity and structural heterogeneity. We first fuse attribute and structural views by coupling nodes' random-walk sequences with neighborhood sampling to jointly extract node attributes and topological context. We then employ a Large Language Model to capture deep semantic information and contextual relationships across multiple text segments, distilling unified themes or high-order community features from the combined attribute-structure representation. Finally, we fine-tune a BERT model on the extracted high-order information to reinforce feature consistency and enable transfer learning for improved generalization. Extensive comparative experiments on real-world datasets demonstrate significant performance improvements over existing mainstream methods, validating the effectiveness of high-order information in alleviating cross-network heterogeneity and confirm the contribution of each component within our deeply integrated multi-view feature learning framework.

Submission Number: 109