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Temporal flows in temporal networks [PDF]
Journal of Computer and System Sciences, 2019 We 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.
Eleni C Akrida +2 more
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Navigation on temporal networks [PDF]
Applied Network ScienceTemporal networks, whose network topology changes over time, are used to represent, e.g., opportunistic mobile networks, vehicle networks, and social contact networks, where two mobile devices (autos or individuals) are connected only when they are close
Omar F. Robledo +2 more
doaj +5 more sources
MODIT: MOtif DIscovery in Temporal Networks [PDF]
Frontiers in Big Data, 2022 Temporal networks are graphs where each edge is linked with a timestamp, denoting when an interaction between two nodes happens. According to the most recently proposed definitions of the problem, motif search in temporal networks consists in finding and
Roberto Grasso +4 more
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Contact-Based Model for Epidemic Spreading on Temporal Networks [PDF]
Phys Rev X, 2019 We present a contact-based model to study the spreading of epidemics by means of extending the dynamic message-passing approach to temporal networks. The shift in perspective from node- to edge-centric quantities enables accurate modeling of Markovian ...
Koher A, Lentz H, Gleeson J, Hövel P.
europepmc +2 more sources
A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks
Entropy, 2023 In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively.
Pietro Cinaglia, Mario Cannataro
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A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks
Entropy, 2023 Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives.
Khushnood Abbas +5 more
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Detecting the driver nodes of temporal networks
New Journal of Physics, 2023 Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions.
Tingting Qin, Gaopeng Duan, Aming Li
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A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction
IEEE Access, 2022 Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data. Link prediction is a near-universal benchmark for new GNN models. Many advanced models such as Dynamic graph neural networks (DGNNs) specifically target
Joakim Skarding +3 more
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Effective Influence Spreading in Temporal Networks With Sequential Seeding
IEEE Access, 2020 The spread of influence in networks is a topic of great importance in many application areas. For instance, one would like to maximise the coverage, limiting the budget for marketing campaign initialisation and use the potential of social influence.
Radosaw Michalski +2 more
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Homogenous mixing and network approximations in discrete-time formulation of a SIRS model
Journal of Biological Dynamics, 2021 A discrete-time deterministic epidemic model is proposed to better understand the contagious dynamics and the behaviour observed in the incidence of real infectious diseases.
Ilaria Renna
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