Journal Information
IEEE Transactions on Network Science and Engineering (TNSE)
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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.
Last updated by Dou Sun in 2021-04-08
Special Issues
Special Issue on Research on Power Technology, Economy and Policy Towards Net-Zero Emissions
Submission Date: 2022-10-31

With the increased concerns about climate change, more and more countries are formulating proper carbon dioxide (CO2) emissions reduction targets based on their practical development stage. To realize the national strategy of decarbonization of primary energy and electric power sector, electric power industries need to undergo reforms to cope with the multiple challenges of carbon emission reduction, marketization, and energy transition. When facing this low-carbon transition problem, it is necessary to change the conventional structure and operation mechanism of the energy and power industries. Likewise, this change will also promote the development and application of digitalization and intelligence in power system. From the perspective of the total amount emission control, the low-carbon transition of the power industry must properly coordinate while providing supports for the decarbonization of the entire national economy. This coordination requires the power industry development to sustain the country's macro policies and financial guidance in industrial structure adjustment and environmental pollution control. It is desired to conduct systematic and in-depth research including how to clearly describe and qualitatively evaluate the impacts and effects of macro policies and economic systems; how to achieve good coordination between the power industry, national macroeconomic strategies, and energy planning strategies in a low-carbon environment; how to measure the economic costs, and quantify the effects of carbon emissions reduction to social benefits; how to properly determine the carbon emissions reduction targets for the power industry, the decarbonization space of each link. At the same time, in the ongoing electricity market reform process, the emerging carbon trading market will have intricate interactions with the low-carbon reform policies of the power industry. Due to restrictions on electricity price policies, power dispatch, power generation and transmission capacity, it is difficult to accurately and quickly deliver carbon impacts caused by carbon prices to end-users. This reduces the effects of setting carbon prices in the power sector on promoting emissions reduction in related industries. Meanwhile, one of the latest trends in power systems involves artificial intelligence technologies supplementing the conventional model-based power system analysis. This Special Issue is intended to encourage scholars and experts to systematically discuss the latest research progress and development trends in “Research on Power Technology, Economy and Policy Towards Net-Zero Emissions” and related energy transition topics, promote in-depth research and share academic and technical achievements. Topics of interest include, but are not limited to, the following: Accurate measuring and computing architecture for carbon emissions from energy networks. Intelligent system modeling and optimization for low-carbon energy networks. Big data analytic frameworks for net-zore emission power networks. Policies, economic measures and path planning to achieve net-zero emissions power networks. Analysis and evaluation of low-carbon networks in all aspects of the power industry, such as telecommunication network, transportation network, etc. Privacy data collection, storage and management for demand side in large-scale energy networks. Technical measures to achieve carbon emission reduction targets in power networks. Digital and intelligent technologies that support the low-carbon transformation of energy networks. Research on the electricity market mechanism supporting net-zero emissions transactive electricity networks.
Last updated by Dou Sun in 2022-06-26
Special Issue on Next-generation Traffic Measurement with Network-wide Perspective and Artificial Intelligence
Submission Date: 2022-12-15

Traffic measurement is deemed as the bedrock of the next-generation network systems. Its function is to monitor network traffic at all protocol layers, from the physical layer to the applications, and to capture traffic patterns, relationships, and anomalies in the time dimension and volume dimension to support fundamental network functions and upper-layer services, such as load balancing, routing, intrusion detection, traffic engineering, and performance diagnosis. Recently, the explosive Internet traffic growth, the emerging networking paradigms, and the surging network service demands have opened new challenges for traffic measurement, which have gained significant attention from both academia and industry. However, the state-of-the-art solutions, which mainly focus on single-point measurement scenarios and derive probabilistic formulas to measure elementary metrics like frequency, cardinality, and persistence, cannot meet the arising heterogeneous and fine-grained measurement requirements on performance, throughput, scalability, response time, and diversity. For example, modern switches can forward packets at extremely high throughput (up to several Gpps), practically two orders of magnitude higher than the throughput of existing sketch solutions. For another example, application-oriented sketches that provide timely and accurate features beyond elementary metrics to applications like traffic engineering and intrusion detection systems can undoubtedly benefit such systems, while the design of such sketches and the derivation of measurement formulas are non-trivial problems. Thus, there is an urgent need for systematic and in-depth research on network-wide and AI-powered traffic measurement methods to meet new network traffic characteristics and support emerging applications. It is expected that the next-generation network management systems will feature network-wide measurement algorithms. The big network data is distributed in nature as the sources and destinations of connections may span the entire network. It is thus essential to aggregate the views of multiple measurement points to build a network-wide perception and capture comprehensive and accurate traffic information. Besides, with a proper task breakdown schema, multiple measurement points can federatively run measurements in a completely parallel and distributed manner, reducing the computation overhead and hardware requirement. Another latest trend involves artificial intelligence technologies that allow seamless aggregation of multi-resolution and heterogeneous network traffic data while advancing traffic measurement systems' design, deployment, and application. Additionally, the interplay between traffic measurement and artificial intelligence is bidirectional. Besides using AI intelligence to power traffic measurement, traffic measurement methods can also aid the AI systems since AI systems are often deployed in a decentralized environment where communication plays an important role. For instance, sketches, a family of traditional measurement methods, can naturally compress the input data and approximate its distribution. It can be used as the medium to transfer gradients among distributed AI systems, striking a tradeoff among information compression, convergence time, and system accuracy. This special issue is intended to encourage scholars and experts to systematically discuss the latest research progress and development trends for next-generation traffic measurement, promote in-depth research, and share academic and technical achievements. The topics of this SI include, but are not limited to: Network-wide traffic measurement algorithms and systems Use of artificial intelligence, machine learning, and data analytics in network traffic measurement and its applications Network-wide and/or AI-powered traffic measurement for the Internet, edge networks, data center networks, cloud-based systems, software-defined networks, online social networks, online services, and next-generation networks Traffic measurement with programmable hardware and software platforms Traffic measurement with privacy preservation and anonymization AI-powered design, simulation, modeling, analysis, and visualization for next-generation traffic measurement Validation and repeatability of network-wide and/or AI-powered traffic measurements, shared datasets, or collaborative platforms Novel applications of network-wide and AI-powered traffic measurement for load balancing, flow scheduling, network management, and network evolution Novel applications of network-wide and AI-powered traffic measurement for security, anomaly/vulnerability/attack detection, and user profiling/privacy Novel applications of traffic measurement for artificial intelligence and machine learning
Last updated by Dou Sun in 2022-06-26
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YearBest Papers
2019Network Maximal Correlation
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