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Graph Neural Networks: Foundation, Frontiers and Applications
Knowledge Discovery and Data Mining, 2023The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-
Lingfei Wu +4 more
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Journal of Medicinal Chemistry, 2020
Hunting for chemicals with favourable pharmacological, toxicological and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the ...
Zhaoping Xiong +10 more
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Hunting for chemicals with favourable pharmacological, toxicological and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the ...
Zhaoping Xiong +10 more
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The rank of a complex unit gain graph in terms of the rank of its underlying graph
Journal of combinatorial optimization, 2017Let Φ=(G,φ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi =(G ...
Yong Lu, Ligong Wang, Qiannan Zhou
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A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks
IEEE Transactions on Cybernetics, 2022Graph representation learning has re-emerged as a fascinating research topic due to the successful application of graph convolutional networks (GCNs) for graphs and inspires various downstream tasks, such as node classification and link prediction ...
Chao Gao +4 more
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Bipartite Consensus for Takagi-Sugeno Fuzzy Uncertain Multi-Agent Systems With Gain Fluctuations
IEEE Transactions on Signal and Information Processing over Networks, 2023This paper examines the problem of bipartite consensus for Takagi-Sugeno fuzzy multi-agent systems subject to uncertainties. The principal intention of this work is to develop a non-fragile controller through which the considered multi-agent system can ...
R. Sakthivel +3 more
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AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification
IEEE Transactions on Network Science and Engineering, 2021Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph ...
Qi Xuan +6 more
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IEEE Transactions on Automatic Control, 2021
This article addresses the decentralized tracking control problem for a class of strong interconnected nonlinear systems with actuator faults. The considered interconnections are allowed to be dominated by some bounding functions, which are linear growth
Hongjun Ma, Lin-Xing Xu
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This article addresses the decentralized tracking control problem for a class of strong interconnected nonlinear systems with actuator faults. The considered interconnections are allowed to be dominated by some bounding functions, which are linear growth
Hongjun Ma, Lin-Xing Xu
semanticscholar +1 more source
Graph-based Path Planning for Autonomous Robotic Exploration in Subterranean Environments
IEEE/RJS International Conference on Intelligent RObots and Systems, 2019This paper presents a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Attuned to the fact that subterranean settings, such as underground mines, are often large-scale networks of narrow tunnel-like and ...
Tung Dang +4 more
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On the determinant of the Laplacian matrix of a complex unit gain graph
Discrete Mathematics, 2018Yi Wang, S. Gong, Yi-Zheng Fan
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