• Learning Strong Graph Neural Networks with Weak Information🔍
• Graph Neural Networks and its Applications🔍
• Explaining Graph Neural Networks via Structure|aware Interaction ...🔍
• Neural Trees for Learning on Graphs🔍
• What is a Neural Network?🔍
• Graph neural network🔍
• What are Graph Neural Networks 🔍
• What Are Graph Neural Networks?🔍

SURER: Structure-Adaptive Unified Graph Neural Network for Multi ...

Specifically, we first design a graph structure learning module to refine the original view-specific attribute graphs, which removes false edges ...

Learning Strong Graph Neural Networks with Weak Information

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs can deteriorate when ...

Graph Neural Networks and its Applications - Seldon

Graph Neural Networks are artificial neural networks developed to process graph structured input data. ... learning models that process ...

Explaining Graph Neural Networks via Structure-aware Interaction ...

... learning models thanks to its strong theoretical foundation. However, for models with structured inputs, such as graph neural networks, existing Shapley ...

Neural Trees for Learning on Graphs - NASA/ADS

In this work, we propose a new GNN architecture -- the Neural Tree. The neural tree architecture does not perform message passing on the input graph, but on a ...

AAGCN: a graph convolutional neural network with adaptive feature ...

Graph neural networks are a type of deep learning models specifically designed for graph-structured data. They have the capability to handle ...

What is a Neural Network? - IBM

Neural networks rely on training data to learn and improve their accuracy over time. ... inputs and the output—can be considered a deep learning algorithm. A ...

Graph neural network - Wikipedia

A graph neural network (GNN) belongs to a class of artificial neural networks for processing data that can be represented as graphs.

What are Graph Neural Networks (GNNs)? - TechTarget

GNN models are typically trained using traditional neural network training methods, such as backpropagation or transfer learning, but are structured ...

What Are Graph Neural Networks? - NVIDIA Blog

Graph neural networks (GNNs) apply the predictive power of deep learning to rich data structures that depict objects and their relationships ...

An Introduction to Graph Neural Networks - Coursera

Explore graph neural networks, a deep-learning method designed to ... These neural networks assume that inputs (like pixels in an image ...

State representation learning using a graph neural network in a 2D ...

... networks is that the mapping from input ... Fidler (2018), NerveNet: Learning Structured Policy with Graph. Neural Networks, in International Conference on ...

The Essential Guide to GNN (Graph Neural Networks) - Cnvrg.io

Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas ...

Multi-Scale Graph Representation Learning with Latent Hierarchical ...

Abstract: A wide variety of deep neural network models for graph-structured data have been proposed to solve tasks like node/graph classification and link ...

What are Graph Neural Networks? - GeeksforGeeks

Graph Neural Network is a modern machine learning technique that is sued to perform various operations on graphical data.

A Beginner's Guide to Graph Neural Networks - V7 Labs

The modified convolutional kernel is represented as: Here, gθ is the set of self-learning parameters. X is an N-dimensional input vector. U is ...

8. Graph Neural Networks - deep learning for molecules & materials

Graph neural networks (GNNs) are a category of deep neural networks whose inputs are graphs and provide a way around the choice of descriptors.

Exploring Graph Neural Networks in Reinforcement Learning

series data is used as input, while GCN is used to capture topological structure. After obtaining time series and spatial features, those are used as input ...

Graph Neural Networks (GNNs) - Comprehensive Guide - viso.ai

The primary goal of GNNs is to learn a representation (embedding) of the graph structure. The GNN captures both the properties of the nodes ( ...

Extended study on atomic featurization in graph neural networks for ...

As input, they use molecular graphs in which vertices represent atoms and edges represent chemical bonds. To put it more precisely, a molecule ...