Cargando…
TemporalGAT: Attention-Based Dynamic Graph Representation Learning
Learning representations for dynamic graphs is fundamental as it supports numerous graph analytic tasks such as dynamic link prediction, node classification, and visualization. Real-world dynamic graphs are continuously evolved where new nodes and edges are introduced or removed during graph evoluti...
Autores principales: | Fathy, Ahmed, Li, Kan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206228/ http://dx.doi.org/10.1007/978-3-030-47426-3_32 |
Ejemplares similares
-
omicsGAT: Graph Attention Network for Cancer Subtype Analyses
por: Baul, Sudipto, et al.
Publicado: (2022) -
GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification
por: Hu, Jinlong, et al.
Publicado: (2021) -
Learning temporal attention in dynamic graphs with bilinear interactions
por: Knyazev, Boris, et al.
Publicado: (2021) -
LaGAT: link-aware graph attention network for drug–drug interaction prediction
por: Hong, Yue, et al.
Publicado: (2022) -
sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure
por: Yan, Ke, et al.
Publicado: (2022)