Are Graph Neural Networks generalizations of Convolutional Neural ...

A Generalization of Convolutional Neural Networks to Graph ...

Abstract. This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured ...

The Graph Neural Network Model

To define a deep neural network over general graphs, we need to generalize both of these approaches and design a framework without such topological ...

Convolutional Neural Networks on Graphs with Fast Localized ...

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are ...

Generalizing Graph Neural Networks on Out-of-Distribution Graphs

Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training graphs and testing graphs.

Full article: A road generalization method using graph convolutional ...

The proposed method uses graph convolutional neural network techniques to construct a road generalization model, and can effectively combine ...

What is the Graph Neural Network? | by Alakh Sharma - Medium

Graph Neural Networks are a type of neural network designed to perform inference on data described by graphs.

Transferability of Spectral Graph Convolutional Neural Networks

It is thus important to transfer ConvNets between graphs. Transferability, which is a certain type of generalization capability, can be loosely defined as ...

Generalization error of graph neural networks in the mean-field regime

This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, ...

Convolutional Neural Networks on Graphs with Fast Localized ...

Graphs can encode complex geometric structures and can be studied with strong mathematical tools such as spectral graph theory [6]. A generalization of CNNs to ...

Understanding Graph Neural Networks with Generalized Geometric ...

These results lay the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable ...

Graph Convolutional Networks —Deep Learning on Graphs

In this post we will see how the problem can be solved using Graph Convolutional Networks (GCN), which generalize classical Convolutional Neural Networks (CNN) ...

VirtuosoResearch/Generalization-in-graph-neural-networks - GitHub

We provide the implementation to evaluate Hessian-based quantities (eg, traces, top-eigenvalues, Hessian vector product) of graph neural networks.

What is Dynamic Graph Neural Networks - Activeloop

A dynamic graph neural network (DGNN) is an extension of graph neural networks (GNNs) designed to handle dynamic graphs, which are graphs that change over time.

Understanding the Representation Power of Graph Neural Networks ...

... generalizations. For example, [28] showed that ... Graph convolution networks [8, 20] generalize the convolutional operation from convolutional neural.

An Overview of Graph Models | Papers With Code

Recently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different ...

Generalization of Graph Neural Networks is Robust to Model Mismatch

Graph neural networks (GNNs) have demonstrated their effectiveness in various tasks supported by their generalization capabilities.

Graph Neural Networks: Models and Applications - Yao Ma

... learning on graph structured data so that downstream tasks can be facilitated. Graph Neural Networks (GNNs), which generalize the deep neural network models ...

Out-of-distribution generalization in graph neural networks

Graphs are one of the most natural representations of many real-world data, such as social networks, chemical molecules, and transportation networks.

From Local Structures to Size Generalization in Graph Neural ...

Graph neural networks (GNNs) can process graphs of different sizes, but their ability to generalize across sizes, specifically from small to large graphs, ...

Generalized Taxonomy-Guided Graph Neural Networks - IJCAI

To address these issues, we propose generalized Taxonomy-Guided Graph. Neural Networks (TG-GNN) to integrate taxonomy into network representation learning. We ...