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User anchor link identification based on integrated similarity decision in multiple heterogeneous social networks

The continuous development of social networks has led to the emergence of various forms of social networks, and people are willing to participate in multiple social networks to enjoy new services. Once a user joins a new social network, a new connection relationship called a user anchor link is formed between users in the two networks. Identifying user anchor links makes sense for many cross-network applications, such as using multiple network information for link recommendations and community discovery. This paper studies the identification problem of user anchor link in multiple heterogeneous social network, and pro-poses a user anchor link identification algorithm in multiple heterogeneous social network based on integrated similarity decision. The algorithm includes two components. One component is to utilize the users’ individual natural attributes and behaviors to decide their similarity; the other component is to decide the user similarity through the improved user friend relationship calculation. Finally, the results of the above two components are integrated to realize the similarity judgment between users, and then identify the users anchor link relationship. Relevant experiments carried out on two heterogeneous social networks show that the pro-posed algorithm outperform other commonly-used baselines on user anchor link identification.