CN-Motifs Perceptive Graph Neural Networks
Graph neural networks (GNNs) have been the dominant approaches for graph representation learning. However, most GNNs are applied to homophily graphs and perform poorly on heterophily graphs. Meanwhile, these GNNs fail to directly capture long-range dependencies and complex interactions between 1-hop...
Guardado en:
Autores principales: | Fan Zhang, Tian-Ming Bu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/270e379a7473422bb382ef7305a6eb05 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Sparse Graph Learning Under Laplacian-Related Constraints
por: Jitendra K. Tugnait
Publicado: (2021) -
KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors
por: Jeong-Hun Kim, et al.
Publicado: (2021) -
Testing biological network motif significance with exponential random graph models
por: Alex Stivala, et al.
Publicado: (2021) -
ON HARMONIOUS COLORING OF TOTAL GRAPHS OF C(Cn), C(K1,n) AND C(Pn)
por: Vivin J,Vernold, et al.
Publicado: (2010) -
Discovering Latent Representations of Relations for Interacting Systems
por: Dohae Lee, et al.
Publicado: (2021)