Cargando…
TSI-GNN: Extending Graph Neural Networks to Handle Missing Data in Temporal Settings
We present a novel approach for imputing missing data that incorporates temporal information into bipartite graphs through an extension of graph representation learning. Missing data is abundant in several domains, particularly when observations are made over time. Most imputation methods make stron...
| Autores principales: | , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2021
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480427/ https://www.ncbi.nlm.nih.gov/pubmed/34604740 http://dx.doi.org/10.3389/fdata.2021.693869 |