Hypergraph convolution and hypergraph attention
Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order...
Main Authors: | Bai, S, Zhang, F, Torr, PHS |
---|---|
Formato: | Journal article |
Idioma: | English |
Publicado: |
Elseveir
2020
|
Títulos similares
-
Link Prediction in Knowledge Hypergraph Combining Attention and Convolution Network
por: PANG Jun, XU Hao, QIN Hongchao, LIN Xiaoli, LIU Xiaoqi, WANG Guoren
Publicado: (2023-11-01) -
Decomposing hypergraphs into k-colorable hypergraphs
por: Gholamreza Omidi, et al.
Publicado: (2014-06-01) -
An analytic approach to sparse hypergraphs: hypergraph removal
por: Henry Towsner
Publicado: (2018-01-01) -
Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods
por: Liyan Zhang, et al.
Publicado: (2022-06-01) -
Hypergraph-Mlp: learning on hypergraphs without message passing
por: Tang, B, et al.
Publicado: (2024)