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Temporal Multivariate Networks
2014Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, the structure of the dynamic graph evolves as vertices and edges are added or removed from the graph.
Daniel Archambault +7 more
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2022
Abstract When a process takes place on an evolving network or this network serves as an evolving substrate of a dynamical system, two time scales naturally emerge: (i) the shortest time of structural changes in a local neighbourhood of each vertex, and (ii) the shortest time (time step) of a process.
Sergey N. Dorogovtsev +1 more
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Abstract When a process takes place on an evolving network or this network serves as an evolving substrate of a dynamical system, two time scales naturally emerge: (i) the shortest time of structural changes in a local neighbourhood of each vertex, and (ii) the shortest time (time step) of a process.
Sergey N. Dorogovtsev +1 more
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Temporal networks: Characterization, motifs and spreading
International School of Physics “Enrico Fermi”, 2019Networks as scaffolds of complex systems are intrinsically dynamic: They grow and shrink, split and merge, as well as there are processes taking place on them like spreading phenomena. As long as the time scale of the change of the network is much slower than that of the processes a static network picture is adequate.
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Temporal flows in temporal networks
Journal of Computer and System Sciences, 2019We introduce temporal flows on temporal networks. We show that one can find the maximum amount of flow that can pass from a source vertex s to a sink vertex t up to a given time in Polynomial time. We provide a static Time-Extended network (TEG) of polynomial size to the input, and show that temporal flows can be decomposed into flows, each moving ...
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Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial
IEEE Communications Surveys and Tutorials, 2021Martin Haenggi, Xiao Lu, Ekram Hossain
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System Identification for Temporal Networks
IEEE Transactions on Network Science and EngineeringModelling temporal networks is an open problem that has attracted researchers from a diverse range of fields. Currently, the existing modelling solutions of time-evolving graphs do not allow us to provide an accurate graph sequence. In this paper, we examine the network dynamics from a system identification perspective. We prove that any periodic graph
Sergey Shvydun, Piet Van Mieghem
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Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism
IEEE Transactions on Neural Networks and Learning Systems, 2022Xuan Guo, Maoguo Gong, Dongxiao He
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IEEE Transactions on Intelligent Transportation Systems, 2023
Yong Zhang, Junbin Gao, Boyue Wang
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Yong Zhang, Junbin Gao, Boyue Wang
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A Novel Prediction-Based Temporal Graph Routing Algorithm for Software-Defined Vehicular Networks
IEEE Transactions on Intelligent Transportation Systems, 2022Liang Zhao +2 more
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T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
IEEE Transactions on Intelligent Transportation Systems, 2020Tao Lin, Yu Liu, Haifeng Li
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