Results 231 to 240 of about 323,205 (262)
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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 ...
Paul Spirakis +2 more
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Artificial Intelligence, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rina Dechter, Itay Meiri, Judea Pearl
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rina Dechter, Itay Meiri, Judea Pearl
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Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Networks (or graphs) are used to represent and analyze large datasets of objects and their relations. Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration. In this tutorial we present models and algorithms for mining temporal networks, i.e., network data with temporal ...
Gionis, Aristides, Rozenshtein, Polina
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Networks (or graphs) are used to represent and analyze large datasets of objects and their relations. Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration. In this tutorial we present models and algorithms for mining temporal networks, i.e., network data with temporal ...
Gionis, Aristides, Rozenshtein, Polina
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IEEE Transactions on Circuits and Systems II: Express Briefs, 2019
Temporal networks are composed of individuals with on-and-off interactions. In the study of human dynamics, a typical interaction is interpreted as a coincidental or forced concurrence of two events. Since human beings’ rationality is bounded, the interaction between sentient individuals is normally investigated under the framework of game theory in ...
Yichao Zhang 0001 +6 more
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Temporal networks are composed of individuals with on-and-off interactions. In the study of human dynamics, a typical interaction is interpreted as a coincidental or forced concurrence of two events. Since human beings’ rationality is bounded, the interaction between sentient individuals is normally investigated under the framework of game theory in ...
Yichao Zhang 0001 +6 more
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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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Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial
IEEE Communications Surveys and Tutorials, 2021Martin Haenggi +2 more
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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 gravity model for important node identification in temporal networks
Chaos, Solitons and Fractals, 2021Cunquan Qu, Jialin Bi, Xiuxiu Zhan
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