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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...
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| Published in: | PLoS One |
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| Main Author: | |
| Format: | Artigo |
| Language: | Inglês |
| Published: |
Public Library of Science
2018
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| Subjects: | |
| Online Access: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6166936/ https://ncbi.nlm.nih.gov/pubmed/30273384 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0204096 |
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