May 19, 2022 · In this paper, we postulate a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as ...
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Jun 20, 2023 · This paper studies the graph structure learning/recovery problem and it can have the potential to solve many real applications (more explanations are required).
We introduce GDNs, a supervised learning NN model capable of recovering sparse latent graph structure from observations of its convolutional mixtures, i.e., ...
This paper [1] proposed the graph deconvolution network (GDN), a neural network unrolled from proximal gradient.
This paper postulates a graph convolutional relationship between the observed and latent graphs, and forms the graph learning task as a network inverse ...
Learning Graph Structure from. Convolutional Mixtures Max Wasserman Dept. of Computer Science University of Rochester Rochester, NY 14627
In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by- ...
Feb 28, 2023 · This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN ...
In the computer graphics community, we can notice a parallel effort of generalizing deep learning architectures to 3D shapes modeled as mani- folds (surfaces).
Aug 10, 2022 · Graph convolutional networks (GCNs) are a special type of graph neural networks (GNNs) that use convolutional aggregations.