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A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks wit...
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| Vydáno v: | Adv Neural Inf Process Syst |
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| Hlavní autoři: | , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
2013
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4230494/ https://ncbi.nlm.nih.gov/pubmed/25400487 |
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