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April 7, 2021: One paper "Graph Neural Networks Inspired by Classical Iterative ... Algorithms for massive data; streaming algorithms; graph algorithms.

24.03.2020 · A Comprehensive Survey on Graph Neural Networks. Abstract: Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space.

27.01.2021 · In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms.

A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2021), 4–24. Google Scholar Cross Ref; Zhipu Xie, Weifeng Lv, Shangfo Huang, Zhilong Lu, and Bowen Du. 2020. Sequential graph neural network for urban road traffic speed prediction. IEEE Access 8 (2020), 63349–63358. Google ...

Rethinking graph neural network search from message-passing. CVPR, 2021. [Cen et al., 2021] Yukuo Cen et al. Cogdl: An extensive toolkit for deep learning on ...

In graph analysis, motivated by the effectiveness of deep learning, graph neural networks (GNNs) are becoming increasingly popular in modeling graph data. Recently, an increasing number of approaches have been proposed to provide explanations for GNNs or to improve GNN interpretability.

A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2021), 4–24. Google Scholar Cross Ref; Zhipu Xie, Weifeng Lv, Shangfo Huang, Zhilong Lu, and Bowen Du. 2020. Sequential graph neural network for urban road traffic speed prediction. IEEE Access 8 (2020), 63349–63358. Google ...

Jan 27, 2021 · In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing ...

Jan 27, 2021 · In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing ...

A Survey of Graph Neural Networks for Electronic Design Automation Daniela Sanchez Lopera´ z, Lorenzo Servadei , Gamze Naz Kiprit , Souvik Hazra , Robert Willey, Wolfgang Eckerz Infineon Technologies AG, Germany,yJohannes Kepler University Linz, Austria, zTechnical University of Munich, Germany Abstract—Driven by Moore’s law, the chip design complexity

(2020) are the most up-to-date survey papers on GNNs and they mainly focus on models of GNN. Wu et al. (2019a) categorize GNNs into four groups: recurrent graph ...

Original Paper; Published: 02 November 2021 ... Graph neural networks are designed to deal with the particular graph-based input and have ...

2021. TLDR. This article provides a comprehensive survey of graph neural networks (GNNs) in each learning setting: supervised, unsupervised, ...

Graph neural networks (GNNs) have been popularly used in ... In this part, we survey the related work on federated learn-ing on graphs, decentralized federated learning, ... federated learning and graph neural networks [Zheng et al., 2021], [Wu et al., 2021]. These models, ...

A Comprehensive Survey on Graph Neural Networks. Abstract: Deep learning has revolutionized many machine learning tasks in recent years, ...

27.01.2021 · In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms.

Dec 14, 2021 · Graph data structure can well express the topology structure of traffic network, so graph model has more development space in the field of traffic prediction. The main purpose of this paper is to provide a comprehensive survey for the graph neural network in the field of traffic prediction. First, the graph model framework was divided into four ...

14.12.2021 · Graph data structure can well express the topology structure of traffic network, so graph model has more development space in the field of traffic prediction. The main purpose of this paper is to provide a comprehensive survey for the graph neural network in the field of traffic prediction. First, the graph model framework was divided into four ...

Computer Science > Machine Learning. arXiv:2108.10733 (cs). [Submitted on 24 Aug 2021 (v1), last revised 8 Sep 2021 (this version, v2)] ...

14.01.2021 · Top Applications of Graph Neural Networks 2021. ... For more results on this topic, take a look at a recent survey and a blog post that studies more …

Graph Neural Network for Traffic Forecasting: A Survey. Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In ...

A Survey of Graph Neural Networks ... 978-1-6654-3166-8/21/$31.00 ©2021 IEEE blocks using Hardware Description Languagess (HDLs) such. 6\VWHP 6SHFLILFDWLRQ

Online:08 October 2021Publication History ... Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their ...

01.01.2021 · Graph Neural Network for Traffic Forecasting: A Survey. Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In ...