Results 201 to 210 of about 3,672,970 (235)

A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks

IEEE Transactions on Cybernetics, 2022
Graph 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, Junyou Zhu, Fan Zhang, Zhen Wang, Xuelong Li   +4 more
semanticscholar   +1 more source

Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism.

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, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, X. Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, M. Zheng   +10 more
semanticscholar   +1 more source

Lock-Gain Based Graph Partitioning

Journal of Heuristics, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong-Hyuk Kim, Byung-Ro Moon
openaire   +2 more sources

Decentralized Adaptive Fault-Tolerant Control for a Class of Strong Interconnected Nonlinear Systems via Graph Theory

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
semanticscholar   +1 more source

AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification

IEEE Transactions on Network Science and Engineering, 2021
Our 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, Jinchao Zhou, Kunfeng Qiu, Zhuangzhi Chen, Dongwei Xu, Shilian Zheng, Xiaoniu Yang   +6 more
semanticscholar   +1 more source

Inertia of complex unit gain graphs

Applied Mathematics and Computation, 2015
Let T = { z ? C : | z | = 1 } be a subgroup of the multiplicative group of all nonzero complex numbers C × . A T -gain graph is a triple ? = ( G , T , ? ) consisting of a graph G = ( V , E ) , the circle group T and a gain function ? : E ? ? T such that ? ( e i j ) = ? ( e j i ) - 1 = ? ( e j i ) ? . The adjacency matrix A(?) of the T -gain graph ? = (
Jianhua Tu, Guihai Yu, Hui Qu
openaire   +2 more sources

Subgroup Switching of Skew Gain Graphs

Fundamenta Informaticae, 2012
Gain graphs are graphs in which each edge has a gain (a label from a group so that reversing the direction of an edge inverts the gain). In this paper we take a generalized view of gain graphs in which the gain of an edge is related to the gain of the reverse edge by an anti-involution, i.e., an anti-automorphism of order at most two.
openaire   +3 more sources

Complex unit gain graphs of rank 2

Linear Algebra and its Applications, 2020
Abstract Let T be the group of all complex numbers z with | z | = 1 . A complex unit gain graph, or simply a T -gain graph, is a triple Φ = ( G , T , φ ) consisting of a graph G = ( V , E ) , the circle group T and a gain function φ : E → → T such that φ ( v i v j
Feng Xu, Qi Zhou, Dein Wong, Fenglei Tian   +3 more
openaire   +2 more sources

Evaluating the gain of a flow graph by the Grassmann algebra

International Journal of Control, 1984
The outgoing branches from a node of Coates' flow graph are interpreted as the elements of column vectors of the system matrix. The Grassmann algebra is used to calculate the exterior or outer product of these column vectors for evaluating the system determinant.
C. F. Chen, M. B. Ahmad
openaire   +2 more sources

Construction of cospectral graphs, signed graphs and $${\mathbb {T}}$$-gain graphs via partial transpose

Journal of Algebraic Combinatorics
In the wake of Dutta and Adhikari, who in 2020 used partial transposition in order to get pairs of cospectral graphs, we investigate partial transposition for Hermitian complex matrices. This allows us to construct infinite pairs of complex unit gain graphs (or T-gain graphs) sharing either the spectrum of the adjacency matrix or even the spectrum of ...
Belardo F., Brunetti M., Cavaleri M., Donno A.   +3 more
openaire   +2 more sources