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Laplacian mixture modeling for network analysis and unsupervised learning on graphs
Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic o...
Autor principal: | Korenblum, Daniel |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166936/ https://www.ncbi.nlm.nih.gov/pubmed/30273384 http://dx.doi.org/10.1371/journal.pone.0204096 |
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