Cargando…
Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning
Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a heavy responsibility to online platforms. Current content analysis methods primarily concentrate on non-textual contents, such as images and videos thems...
Autores principales: | Huang, Ru, Chen, Zijian, He, Jianhua, Chu, Xiaoli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963052/ https://www.ncbi.nlm.nih.gov/pubmed/35214304 http://dx.doi.org/10.3390/s22041402 |
Ejemplares similares
-
Understanding microbiome dynamics via interpretable graph representation learning
por: Melnyk, Kateryna, et al.
Publicado: (2023) -
Graph representation learning
por: Hamilton, William L
Publicado: (2020) -
Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models
por: Huang, Yicong, et al.
Publicado: (2022) -
Compressed graph representation for scalable molecular graph generation
por: Kwon, Youngchun, et al.
Publicado: (2020) -
Unsupervised generative and graph representation learning for modelling cell differentiation
por: Bica, Ioana, et al.
Publicado: (2020)