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...

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Detalles Bibliográficos
Autores principales: Gordon, David, Petousis, Panayiotis, Zheng, Henry, Zamanzadeh, Davina, Bui, Alex A.T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Big Data
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

Internet

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

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