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• W2592334341 abstract "Social ties are crucial to understand group behaviors in social networks. Since users rarely label their friends explicitly in social networks, characterizing social ties and understanding their strength of these ties is a critical problem. In this paper, we apply logistic regression model to infer the strength of social ties based on the structural features of social networks, and we produce two new features: cliqueness and linkness. In addition, we use the Strong Triadic Closure principle as a global constraint. We test our model on four different datasets: Facebook, American College Football, Les Miserables and Zachary's Karate Club. Exper-imental results demonstrate that the performance of our model with structural features is satisfactory." @default.
• W2592334341 created "2017-03-16" @default.
• W2592334341 creator A5045940028 @default.
• W2592334341 creator A5052406901 @default.
• W2592334341 date "2016-06-01" @default.
• W2592334341 modified "2023-09-26" @default.
• W2592334341 title "Using Structural Features to Characterize Social Ties" @default.
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• W2592334341 doi "https://doi.org/10.1109/dsc.2016.63" @default.
• W2592334341 hasPublicationYear "2016" @default.
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