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Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection...
The process of identifying and assigning the relationship between two bodies of text is referred to as stance classification. Given a headline and the corresponding body they are compared and their relationship is classified into one of the following two classes — unrelated or related where related is further divided into agree, disagree and discuss. In this article, data is collected from news articles...
The paper described the structure comparison of Bayesian Belief Network models for individual behavior rate estimate based on data about the last episodes of that behavior. We compared two types of network structures: expert-based and data-based. For model learning and evaluation we used data from social network VKontakte about episodes of publishing posts. The sample size was 3803 users with 785066...
Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. The user diversity, dynamic behaviors, and hidden social influences make the problem more challenging. In this work, we propose a deep learning model named Social Restricted Boltzmann Machine (SRBM) for...
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