IEEE Trans Neural Netw Learn Syst - Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data.

Tópicos

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{ network(2748) neural(1063) input(814) }
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{ method(2212) result(1239) propos(1039) }
{ take(945) account(800) differ(722) }
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{ detect(2391) sensit(1101) algorithm(908) }
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Resumo

Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For modeling cluster structures of networks, the infinite relational model (IRM) was proposed as a Bayesian nonparametric extension of the stochastic block model. In this brief, we derive the inference algorithms for the IRM of network data based on the variational Bayesian (VB) inference methods. After showing the standard VB inference, we derive the collapsed VB (CVB) inference and its variant called the zeroth-order CVB inference. We compared the performances of the inference algorithms using six real network datasets. The CVB inference outperformed the VB inference in most of the datasets, and the differences were especially larger in dense networks.

Resumo Limpo

network data show relationship among one kind object social network hyperlink web mani statist model propos analyz data model cluster structur network infinit relat model irm propos bayesian nonparametr extens stochast block model brief deriv infer algorithm irm network data base variat bayesian vb infer method show standard vb infer deriv collaps vb cvb infer variant call zerothord cvb infer compar perform infer algorithm use six real network dataset cvb infer outperform vb infer dataset differ especi larger dens network

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