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IEEE Transactions on Network Science and Engineering (TNSE)

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Factor de Impacto:
7.9
Editor:
IEEE
ISSN:
2334-329X
Vistas:
35409
Seguidores:
15

Solicitud de Artículos

IEEE Transactions on Network Science and Engineering (TNSE) is an academic journal published by IEEE. (ISSN 2334-329X, impact factor 7.9).

The IEEE Transactions on Network Science and Engineering is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks. The core topics covered include: Network Sampling and Measurement; Learning of Network Topology; Modeling and Estimation of Network Dynamics; Network Inference; Models of Complex Networks; Modeling of Network Evolution; Network Design; Consensus, Synchronization and Control of Complex Networks; Interactions between and Co-evolution of Different Genres of Networks; Community Formation and Detection; Complex Network Robustness and Vulnerability; Network Interdependency and Cascading Failures; Searching in Complex Networks; Information Diffusion and Propagation; Percolation and Diffusion on Networks; Epidemiology in Complex Systems.
Última actualización por Dou Sun el

Los Mejores Artículos

AñoLos Mejores Artículos
2019Network Maximal Correlation

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