Abstract
As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).
Highlights
A product of the Web 2.0 era, today’s social networks provide people with a rich variety of social services
In order to solve the above problems associated with the weight assignment scheme, the present paper proposes a two-level information entropy-based weight assignment method
This paper proposes a two-level information entropy-based across social network user identification algorithm (TIW-UI)
Summary
A product of the Web 2.0 era, today’s social networks provide people with a rich variety of social services. User identification refers to the process of identifying virtual accounts that belong to the same entity user across different social networks. The research work based on user profile information mainly focuses on specific attribute information provided by users (e.g., username, birthday, and interests) [2]. Related works based on network topology information have mainly focused on using a user’s friend relationships to identify their accounts across different social networks [3]; Wireless Communications and Mobile Computing these connections between friend relationships are often sparse, meaning that this method encounters certain limitations when applied. There are a number of largely similar phenomena involved in the process of filling in profile information across different social networks, which provides a reliable basis for user identification.
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