Explore the vast range of titles available.

User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them with emerging countermeasures including adversarial injection, structural perturbation, and identity obfuscation. To bridge these two threads, we introduce a modality-oriented taxonomy and a formal game-theoretic framing that casts cross-network mapping as a contest between mappers and anti-mappers. This framing allows us to construct a cross-modality dependency matrix, which reveals structural information as the most contested signal, identifies node injection as the most robust defensive strategy, and points to multimodal integration as a promising direction. Our survey underscores the need for balanced, privacy-preserving identity inference and provides a foundation for future research on the adversarial dynamics of social identity mapping and defense.

User identity linkage (UIL) aims to link identities belonging to the same individual across various platforms. While numerous methods have been proposed for paired or multiple platforms, UIL is still a non-trivial task due to the following challenges. (1) How to alleviate the sparsity and incompleteness of user information from different platforms? (2) How can UIL approaches achieve high effectiveness while still maintaining low complexity in multi-platform scenarios? In light of these challenges, we propose MMUIL (enhancing multi-platform user identity linkage with multi-information), a novel model excelling in high effectiveness while still maintaining low complexity. The model consists of a Multi-Information Embedding (MIE) module and a Partially Shared Adversarial Learning (PSAL) module. Specifically, for the first challenge, MIE simultaneously considers the token sequence semantics in usernames and the structural information of multi-type networks (i.e., homogeneous and heterogeneous networks). To address the second challenge, the adversarial learning-based PSAL decreases the complexity with shared partial parameters (i.e., shared generators). Meanwhile, to enhance the model’s effectiveness, PSAL exploits an attention mechanism to mitigate the disadvantages of shared partial parameters, such as partial information loss and noise introduction, while integrating the above multi-information intensively. The extensive experiments conducted on two real-world datasets demonstrate that our proposed model MMUIL significantly outperforms the state-of-the-art methods.

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 fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform 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, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.

User Identity Linkage (UIL) has emerged as a focal point of research in the field of network analysis and plays a critical role in the governance of cyberspace; related technologies can also be extended for applications in traffic safety and traffic management. The traditional random walk-based UIL method has achieved a balance between performance and interpretability, but it still faces several challenges, such as low discriminability of nodes, instability of feature extraction, and missing features in matching scenarios. To address these challenges, this paper presents Adap-UIL, a multi-feature UIL framework based on an Adaptive Graph Walk. First, we design and implement an Adaptive Graph Walk method based on the Restarted Affinity Coefficient (RAC), which enhances both the neighborhood and higher-order features of nodes, and then we integrate cross-network features to form Adap-UIL with a more enriched node representation, facilitating user identity linkage. Experimental results on real datasets show that the Adap-UIL model outperforms the benchmark models, especially in the P@5 and P@10 metrics by 5 percentage points, and it captures key features more efficiently and effectively.

User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.

User identity linkage (UIL) refers to identifying user accounts belonging to the same identity across different social media platforms. Most of the current research is based on text analysis, which fails to fully explore the rich image resources generated by users, and the existing attempts touch on the multimodal domain, but still face the challenge of semantic differences between text and images. Given this, we investigate the UIL task across different social media platforms based on multimodal user-generated contents (UGCs). We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation (EUIL) approach. The method first generates captions for user-posted images with the BLIP model, alleviating the problem of missing textual information. Subsequently, we extract aligned text and image features with the CLIP model, which closely aligns the two modalities and significantly reduces the semantic gap. Accordingly, we construct a set of adapter modules to integrate the multimodal features. Furthermore, we design a temporal weight assignment mechanism to incorporate the temporal dimension of user behavior. We evaluate the proposed scheme on the real-world social dataset TWIN, and the results show that our method reaches 86.39% accuracy, which demonstrates the excellence in handling multimodal data, and provides strong algorithmic support for UIL.

Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.

The Internet of Things (IoT) devices spawn growing diverse social platforms and online data at the network edge, propelling the development of cross-platform applications. To integrate cross-platform data, user identity linkage is envisioned as a promising technique by detecting whether different accounts from multiple social networks belong to the same identity. The profile and social relationship information of IoT users may be inconsistent, which deteriorates the reliability of the effectiveness of identity linkage. To this end, we propose a t opic and k nowledge-enhanced m odel for edge-enabled IoT user identity linkage across social networks, named TKM, which conducts feature representation of user generated contents from both post-level and account-level for identity linkage. Specifically, a topic-enhanced method is designed to extract features at the post-level. Meanwhile, we develop an external knowledge-based Siamese neural network for user-generated content alignment at the account-level. Finally, we show the superiority of TKM over existing methods on two real-world datasets. The results demonstrate the improvement in prediction and retrieval performance achieved by utilizing both post-level and account-level representation for identity linkage across social networks.

Random-walk-based graph representation methods have been widely applied in User Identity Linkage (UIL) tasks, which links overlapping users between two different social networks. It can help us to obtain more comprehensive portraits of criminals, which is helpful for improving cyberspace governance. Yet, random walk generates a large number of repeating sequences, causing unnecessary computation and storage overhead. This paper proposes a novel method called Edge-Removing Walk (ERW) that can replace random walk in random-walk-based models. It removes edges once they are walked in a walk round to capture the l−hop features without repetition, and it walks the whole graph for several rounds to capture the different kinds of paths starting from a specific node. Experiments proved that ERW can exponentially improve the efficiency for random-walk-based UIL models, even maintaining better performance. We finally generalize ERW into a general User Identity Linkage framework called ERW-UIL and verify its performance.

Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.