Nov 23, 2018 · We propose a novel multigraph network that learns from multi-relational graphs. We model learned edges with abstract meaning and experiment with different ways ...
This work revisiting a particular family of spectral graph networks, Chebyshev GCNs, shows its efficacy in solving graph classification tasks with a ...
(PDF) Spectral Multigraph Networks for Discovering and Fusing ...
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Sep 12, 2024 · Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules ... Spectral Graph Convolutional Networks (GCNs) are a generalization of ...
Nov 23, 2018 · Spectral Graph Convolutional Networks (GCNs) are a generalization of convo- lutional networks to learning on graph-structured data.
Marinka Zitnik, Monica Agrawal, Jure Leskovec. Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules. NeurIPS Workshop 2018. paper.
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules. Mohamed R. Amer, Boris Knyazev, Xiao Lin, Graham W. Taylor. 22 Nov 2018.
Spectral multigraph networks for discovering and fusing relationships in molecules. B Knyazev, X Lin, MR Amer, GW Taylor. arXiv preprint arXiv:1811.09595 ...
Taylor, Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules, NIPS Workshop on Machine Learning for Molecules and Materials, 2018 ...