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Network data on connections between corporate actors and entities - for instance through co-ownership ties or elite social networks - are increasingly available to researchers interested in probing the many important questions rel...
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Network data on connections between corporate actors and entities - for instance through co-ownership ties or elite social networks - are increasingly available to researchers interested in probing the many important questions related to the study of modern capitalism. Given the analytical challenges associated with the nature of the subject matter, variable data quality and other problems associated with currently available data on this scale, we discuss the promise and perils of using big corporate network data (BCND). We propose a standard procedure for helping researchers deal with BCND problems. While acknowledging that different research questions require different approaches to data quality, we offer a schematic platform that researchers can follow to make informed and intelligent decisions about BCND issues and address these through a specific work-flow procedure. For each step in this procedure, we provide a set of best practices for how to identify, resolve and minimize the BCND problems that arise.
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By a News Reporter-Staff News Editor at Network Daily News – Research
findings on data networks are discussed in a new report. According to news reporting originating from Rice
University by NewsRx correspondents, research stat...
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By a News Reporter-Staff News Editor at Network Daily News – Research
findings on data networks are discussed in a new report. According to news reporting originating from Rice
University by NewsRx correspondents, research stated, “Semantic segmentation for scene understanding
is nowadays widely demanded, raising significant challenges for the algorithm efficiency, especially its
applications on resource-limited platforms.”
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Data centers are growing exponentially (in number and size) to accommodate the escalating user and application demands. Likewise, the concerns about the environmental impacts, energy needs, and electricity cost of data centers are...
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Data centers are growing exponentially (in number and size) to accommodate the escalating user and application demands. Likewise, the concerns about the environmental impacts, energy needs, and electricity cost of data centers are also growing. Network infrastructure being the communication backbone of the data center plays a pivotal role in the data center's scalability, performance, energy consumption, and cost. Research community is endeavoring hard to overcome the challenges faced by the legacy Data Center Networks (DCNs). Serious efforts have been made to handle the problems in various DCN areas. This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs. The authors explored: (a) DCN architectures (electrical, optical, and hybrid), (b) network traffic management and characterization, (c) DCN performance monitoring, (d) network-aware resource allocation, (e) DCN experimentation techniques, and (f) energy efficiency. The survey presents an overview of the ongoing research in the broad domain of DCNs and highlights the challenges faced by the DCN research community.
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Trends change rapidly in today's world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symb...
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Trends change rapidly in today's world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that "shine." That is, at a specific time interval in a network's life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network's topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society.
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To protect the information from being intercepted by third parties during the network communication process, this paper proposes a new type of data steganography technology based on network data flow. Using the network protocol it...
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To protect the information from being intercepted by third parties during the network communication process, this paper proposes a new type of data steganography technology based on network data flow. Using the network protocol itself and the relationship between data packets in the entire network data stream to perform network data steganography, transfer hidden data, and perform secondary identity authentication. Different from the traditional steganography method, this method can encode the hidden data and send the interval value by embedding the data packet, thereby hiding and transmitting the hidden data. In this technology, the operation of hidden data does not affect the user's access request for real network data, and it can perform processes such as hidden data transfer and secondary authentication without the user being able to detect it. Through experimental verification and evaluation, our method improves the concealment of the steganographic channel, is not easy to attract attention and has no obvious statistical characteristics of the traffic, and can improve the concealment and robustness of the steganography technology based on network data streams.
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By extending and instantiating an existing formal task framework, we define a task taxonomy and task design space for temporal graph visualisation. We discuss the process involved in their generation, and describe how the design s...
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By extending and instantiating an existing formal task framework, we define a task taxonomy and task design space for temporal graph visualisation. We discuss the process involved in their generation, and describe how the design space can be ‘sliced and diced’ into multiple overlapping task categories, requiring distinct visual techniques for their support. The approach addresses deficiencies in the task literature, offering domain independence, greater task coverage, and unambiguous task specification. The taxonomy and design space capture tasks for temporal graphs, and also static graphs, multivariate graphs, and graph comparison, and will be of value in the design and evaluation of temporal graph visualisation systems.
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Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and as...
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Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the 'cloud computing' paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures.
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Datacenter networks have attracted a lot of research interest in the past few years. BCube is proved to be a promising scheme due to its low cost. By using a recursive construction scheme, BCube can exponentially scale a datacente...
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Datacenter networks have attracted a lot of research interest in the past few years. BCube is proved to be a promising scheme due to its low cost. By using a recursive construction scheme, BCube can exponentially scale a datacenter. Industry experiences, however, articulate the importance of incremental expansion of datacenter. In this article, the authors show that BCube's expanding scheme suffers low utilization of switch ports. They propose IBCube, a novel economical design for incrementally building datacenter networks. The insight is that: by letting the number of switches in each BCube layer equal the number of the building blocks, the authors can enable the switch ports to be fully utilized to support the total number of network interface cards of the deployed servers in the datacenters. Accordingly, their IBCube designs a novel automatic port allocation scheme. Simulation results show that the IBCube design reduces the budget for the datacenter networks by 94% as well as improves the packet delay and throughput by 10.3% and 11.5%, respectively, compared to the previous partial BCube design.
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Vehicular Ad hoc NETworks (VANETs) have become a leading technology receiving great attention from various research communities as a pivotal infrastructure for data dissemination in intelligent transportation systems. Data dissemi...
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Vehicular Ad hoc NETworks (VANETs) have become a leading technology receiving great attention from various research communities as a pivotal infrastructure for data dissemination in intelligent transportation systems. Data dissemination in VANET is a challenging task due to high dynamics in topology, mobility, and links connection. Internet model (i.e., TCP/IP) is inefficient for VANET data dissemination due to the host/address-centric, and connection-oriented communication mechanism that is fundamentally designed for stable wired networks. Recently, Named Data Networking (NDN) paradigm has been used as a promising perfect-enabler underlying vehicular communication model, i.e., Vehicular Named Data Networking (V-NDN) model. In NDN, the nodes communication involves named-based datacentric operations decoupled from the data provider address/location. Several V-NDN data dissemination schemes have been proposed. In this article, we provide a comprehensive survey representing a thorough-critical presentation of recently proposed V-NDN data dissemination solutions and introduce a new fine-grained taxonomy for these solutions. Then, a qualitative comparison of the reviewed solutions based on several parameters is provided. We also suggest a unified performance evaluation metrics in this domain. Finally, we present the open problems in V-NDN data dissemination and highlight the directions of future-oriented solutions. This comprehensive and self-contained survey can contribute to the exploration and understanding of this research domain. Consequently, the future solutions in the aspects of unresolved problems and inefficient resolutions may be directed towards new solving methods. (C) 2021 Elsevier Inc. All rights reserved.
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This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involvi...
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This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involving multiparty network data sharing with Trusted Third Party (TTP) is proposed. Simulations are then conducted using network data from different sources, and show that the measurement indicators defined in this paper can adequately quantify the privacy of the network. In particular, it can indicate the effect of the auxiliary information of the adversary on privacy.
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