CayleyNets : graph convolutional neural networks with complex rational spectral filters
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In...
Main Authors: | Levie, Ron, Monti, Federico, Bresson, Xavier, Bronstein, Michael M. |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/139445 |
Similar Items
-
Molecular geometric deep learning
by: Shen, Cong, et al.
Published: (2024) -
Poisson kernel: avoiding self-smoothing in graph convolutional networks
by: Yang, Ziqing, et al.
Published: (2022) -
Explainable image recognition with graph-based feature extraction
by: Azam, Basim, et al.
Published: (2025) -
When convolutional network meets temporal heterogeneous graphs: an effective community detection method
by: Zheng, Yaping, et al.
Published: (2023) -
A multisensory interaction framework for human-cyber–physical system based on graph convolutional networks
by: Qi, Wenqian, et al.
Published: (2024)