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Analyzing the User Profile Linkage across Different Social Network Platforms

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Social media analysis is to link up all the data of the same user across different social platforms, which is vital to business intelligence by gathering social data. This paper proposes HYDRA framework with k-mean clustering which comprises the social media networks which measures two users refer to one person when one of their attributes is identical. The action of the user accounts are formed as a cluster by using k-mean clustering and thus the cluster has a data about the user where it mean to be efficient when proliferation of user increasing. Statistical models of topic distribution constructing structural consistency graph to evaluate the high-order structure consistency. Finally, discovering the mapping function by multi-objective optimization compiled both the supervised learning and the cross platform structure consistency maximization. Hence, this model is able to find the hidden relationships of group of users with high delivery data speed.
Keywords:Heterogeneous behavior, k-mean clustering, multi-objective optimization, social identity linkage, structure consistency, user behavior trajectory.


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