A survey of graph neural networks in various learning paradigms

This article introduces preliminary knowledge regarding GNNs and comprehensively surveys GNNs in different learning paradigms.

In the last decade, deep learning has reinvigorated the machine learning field. It has solved many problems in computer vision, speech recognition, ...

A Survey of Graph Neural Networks in Various Learning Paradigms ...

This article provides a comprehensive survey of graph neural networks (GNNs) across different learning paradigms, including supervised, unsupervised, semi- ...

A survey of graph neural networks and their industrial applications

[15] provide a review of heterogeneous network ... et al. A survey of graph neural networks in various learning paradigms: methods, applications, ...

[PDF] A Comprehensive Survey on Graph Neural Networks

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges · Lilapati WaikhomRipon Patgiri. Computer Science.

(PDF) Survey on Graph Neural Networks - ResearchGate

... A survey of graph neural networks in. various learning paradigms: methods, applications, and challenges,''. Artif. Intell. Rev., vol. 56, no ...

Graph Neural Networks: Methods, Applications, and Opportunities

Our work focus on all the learning settings, contrary to various surveys that concentrate on a single learning setting. • Further, each ...

[PDF] Graph Neural Networks: A Review of Methods and Applications

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges · Lilapati WaikhomRipon Patgiri. Computer Science.

naganandy/graph-based-deep-learning-literature - GitHub

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges · Graph Neural Networks in IoT: A Survey · A Survey on ...

A Comprehensive Survey on Graph Neural Networks - IEEE Xplore

ized by various end-to-end deep learning paradigms, e.g., convolutional neural networks (CNNs) [6], recurrent neural networks (RNNs) [7], and autoencoders [8].

A Comprehensive Survey on Deep Graph Representation Learning

... many deep graph representation learning algorithms and graph neural networks developed recently. In this section, we provide a comprehensive review of graph ...

Graph Neural Networks: Methods, Applications, and Opportunities

... various advances in deep learning to graph data-based tasks. This article provides a comprehensive survey of graph neural networks (GNNs) in ...

A review of graph neural networks: concepts, architectures ...

Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks.

A Survey of Graph Neural Networks for Recommender Systems

There are some other surveys focusing on specific techniques of recommendation, such as automated machine learning [Zheng et al., 2022a] and self-supervised ...

A Survey on Graph Neural Networks for Microservice-Based Cloud ...

Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision.

Graph Neural Networks: Graph Structure Learning - SpringerLink

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges. Article 23 November 2022. A review of ...

Graph Neural Networks - tutorials and resources - Fast.ai Forums

I'm thinking of training distributed on Intelligence Processing Units - IPUs, as well as using several GPUs simultaneously. For TF fans, there ...

A Survey of Graph Neural Networks and Their Industrial Applications

L Waikhom, A survey of graph neural networks in various learning paradigms: methods, applications, and challenges, Artificial Intelligence Review, № 56, с.

A Survey of Computationally Efficient Graph Neural Networks ... - MDPI

MPNNs serve as a general paradigm for GNNs, facilitating the exchange of information between nodes to learn node and edge features effectively. Specific GNN ...

Graph Neural Networks and Reinforcement Learning: A Survey

This chapter tries to examine different types of applying GNN and DRL techniques in the most common representations of multi-agent problems and their ...