Cross-Network User Identity Linkage Method with Deep Learning Based on SDNE Embedding Representation

doi:10.3778/j.issn.1673-9418.2401047

Abstract

Abstract: Mining the correlation between massive virtual identities and determining the identity of different virtual identities are of great significance for accurate user recommendation, abnormal user detection, public opinion control and so on. In order to determine whether users from two different social networks belong to the same natural person, a deep learning algorithm based on SDNE (structural deep network embedding) embedding expression (eSUIL) is proposed to solve the problem of cross-network user identity linkage, and a unified framework is constructed. Firstly, the user relationship in the social network is extended, and then the idea of SDNE model is used to embed learning of user nodes in different networks, and the user nodes are mapped into a low-dimensional vector space. Secondly, the deep neural network is used to construct the mapping function to obtain the accurate expression of user nodes. Finally, the similarity between user nodes is calculated to align users, so as to realize user identity linkage across social networks. In order to improve the accuracy of user identity linkage, the user name information is also introduced as an auxiliary judgment. Experimental verification is conducted on the real social network dataset and synthetic network datasets, and the experimental results are more than 8 percentage points higher than the baseline algorithms PALE (predict anchor links via embedding), CLF (collective link fusion) and Deeplink in accuracy and F1 value, indicating that the eSUIL algorithm proposed in this paper has excellent performance in user identity linkage. It can accurately associate the same user identity in different networks.

Key words: network embedding, social network, deep learning, user identity linkage

摘要： 挖掘海量虚拟身份之间的关联关系，确定不同虚拟身份的同一性对于用户精准推荐、异常用户检测、舆情管控等具有重要意义。为了判别多个不同虚拟网络身份是否为同一个人所拥有，提出了一种基于SDNE嵌入表达的深度学习算法（eSUIL）来解决跨网络用户身份关联的问题，并构建了一个跨网络用户身份关联统一框架。对社交网络中的用户关系进行扩展，借鉴SDNE模型的思想对不同网络中的用户节点进行嵌入学习，将用户节点映射到低维向量空间中。使用深度神经网络构造映射函数得到用户节点的准确表达。计算用户节点间的相似度以进行用户对齐，从而实现跨社交网络的用户身份关联。为了提高身份关联的准确性，还引入用户名信息作为辅助判断。在真实社交网络数据集和人工合成网络数据集上分别进行实验验证，实验结果表明，对比基线算法PALE、CLF以及Deeplink，在准确率和F1值上均提高了8个百分点以上，表明提出的eSUIL算法在身份关联方面具有优异的性能，能够准确关联不同网络中的同一用户身份。

关键词: 网络嵌入, 社交网络, 深度学习, 身份关联

CHENG Jialin, YUAN Deyu, SUN Zeyu, CHEN Ziyan. Cross-Network User Identity Linkage Method with Deep Learning Based on SDNE Embedding Representation[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(2): 417-428.

程佳琳, 袁得嵛, 孙泽宇, 陈梓彦. 基于SDNE嵌入表达的深度学习跨网络身份关联方法[J]. 计算机科学与探索, 2025, 19(2): 417-428.

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