Latent feature models for large-scale link prediction

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چکیده

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Latent feature models for large-scale link prediction

*Correspondence: [email protected] Department of Computer Science & Technology, Center for Bio-Inspired Computing Research, Tsinghua National Lab for Information Science & Technology, State Key Lab of Intelligence Technology & System, Tsinghua University, 100084 Beijing, China Abstract Link prediction is one of the most fundamental tasks in statistical network analysis, for which latent fea...

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As the availability and importance of relational data—such as the friendships summarized on a social networking website—increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine learning community has focused on laten...

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Latent Feature Models for Link Prediction

In recent years, several Bayesian models have been developed for link prediction in relational data. Models such as the Infinite Relational Model (IRM) [3] and the Mixed-Membership Stochastic Blockmodel (MMSB) [1] assume that there exists a set of latent classes that each object we observe can belong to and that each object either belongs to a single class or has a distribution over the classes...

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Max-Margin Nonparametric Latent Feature Models for Link Prediction

Link prediction is a fundamental task in statistical network analysis. Recent advances have been made on learning flexible nonparametric Bayesian latent feature models for link prediction. In this paper, we present a max-margin learning method for such nonparametric latent feature relational models. Our approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to d...

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ژورنال

عنوان ژورنال: Big Data Analytics

سال: 2017

ISSN: 2058-6345

DOI: 10.1186/s41044-016-0016-y