Journal articles: 'Networks'

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Kim, Junki. "Networks, Network Governance, and Networked Networks." International Review of Public Administration 11, no. 1 (July 2006): 19–34. http://dx.doi.org/10.1080/12294659.2006.10805075.

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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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Shydlovskyi, P., and Ya Morozova. "FOREWORD: Network Approach for Studying the Prehistoric Networks." Vita Antiqua 1, no. 10 (2018): 6–12. http://dx.doi.org/10.37098/2519-4542-2018-1-10-6-12.

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THATIPAMULA RAJU, THATIPAMULA RAJU, and D. DEEPIKA RANI D. DEEPIKA RANI. "Achieving Network Level Privacy in Wireless Sensor Networks." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 183–87. http://dx.doi.org/10.15373/22778179/aug2013/61.

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Yiyuan Xie, Yiyuan Xie, and Zhu Yang Zhu Yang. "All-optical network interface from backbone networks to local area networks based on semiconductor optical amplifiers." Chinese Optics Letters 11, no. 11 (2013): 110605–8. http://dx.doi.org/10.3788/col201311.110605.

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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (December 31, 2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.

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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.

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Yi-Wei Ma, Yi-Wei Ma, Jiann-Liang Chen Yi-Wei Ma, Yu-Liang Tang Jiann-Liang Chen, and Kuan-Hung Lai Yu-Liang Tang. "Towards Adaptive Network Resource Orchestration for Cognitive Radio Networks." 網際網路技術學刊 23, no. 5 (September 2022): 1087–97. http://dx.doi.org/10.53106/160792642022092305017.

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This work proposes an adaptive resource orchestration system for a Wireless Local Area Network (WLAN) that is based on the operating principle of Cognitive Radio (CR) technology. By collecting environmental parameters, including the retransmission rate and the channel occupancy rate, the proposed system has “knowledge” of overall transmission behavior and can regulate transmission resources. An Adaptive Connection Assignment (ACA) mechanism is proposed for end devices; it find out target end devices with poor transmission performance, analyzes their alternative Access Point (AP) availability and causes them to change connections to improve transmission performance. An Adaptive Channel Utilization (ACU) mechanism is designed for APs to identify a target AP that is suffering from interference, to analyze its alternative channel availability and to require it to change its working channel to improve transmission efficiency. Results of simulations of various scenarios indicate that the throughput of end devices is increased by 15 to 24%, the throughput of APs is increased by 6 to 47% and the retransmission rate of APs is reduced by 0.4 to 5.3%.

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HRABCAK, David, and Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS." Acta Electrotechnica et Informatica 19, no. 3 (December 4, 2019): 39–43. http://dx.doi.org/10.15546/aeei-2019-0022.

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Zhang, Chengjun, Yi Lei, Xinyu Shen, Qi Li, Hui Yao, Di Cheng, Yifan Xie, and Wenbin Yu. "Fragility Induced by Interdependency of Complex Networks and Their Higher-Order Networks." Entropy 25, no. 1 (December 23, 2022): 22. http://dx.doi.org/10.3390/e25010022.

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The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order networks can explore hub structures in transportation networks and information dissemination units in neuronal networks. Therefore, the destruction of the connectivity of higher-order networks will cause significant damage to network functionalities. Meanwhile, previous works pointed out that the function of a complex network depends on the giant component of the original(low-order) network. Therefore, the network functionality will be influenced by both the low-order and its corresponding higher-order network. To study this issue, we build a network model of the interdependence of low-order and higher-order networks (we call it ILH). When some low-order network nodes fail, the low-order network’s giant component shrinks, leading to changes in the structure of the higher-order network, which further affects the low-order network. This process occurs iteratively; the propagation of the failure can lead to an eventual network crash. We conducted experiments on different networks based on the percolation theory, and our network percolation results demonstrated a first-order phase transition feature. In particular, we found that an ILH is more fragile than the low-order network alone, and an ILH is more likely to be corrupted in the event of a random node failure.

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Kudĕlka, Miloš, Šárka Zehnalová, Zdenĕk Horák, Pavel Krömer, and Václav Snášel. "Local dependency in networks." International Journal of Applied Mathematics and Computer Science 25, no. 2 (June 1, 2015): 281–93. http://dx.doi.org/10.1515/amcs-2015-0022.

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Abstract Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.

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Levin, Ilya, Mark Korenblit, and Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT." Problems of Education in the 21st Century 54, no. 1 (June 20, 2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.

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Yan, Erjia, and Ying Ding. "Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other." Journal of the American Society for Information Science and Technology 63, no. 7 (May 31, 2012): 1313–26. http://dx.doi.org/10.1002/asi.22680.

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Muneepeerakul, Rachata, Jeffrey C. Johnson, Michael J. Puma, and Michael A. Zurek-Ost. "Triadic signatures of global human mobility networks." PLOS ONE 19, no. 2 (February 23, 2024): e0298876. http://dx.doi.org/10.1371/journal.pone.0298876.

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Global refugee and migrant flows form complex networks with serious consequences for both sending and receiving countries as well as those in between. While several basic network properties of these networks have been documented, their finer structural character remains under-studied. One such structure is the triad significance profile (TSP). In this study, the TSPs of global refugee and migrant flow networks are assessed. Results indicate that the migrant flow network’s size and TSP remain stable over the years; its TSP shares patterns with social networks such as trade networks. In contrast, the refugee network has been more dynamic and structurally unstable; its TSP shares patterns with networks in the information-processing superfamily, which includes many biological networks. Our findings demonstrate commonality between migrant and social networks as well as between refugee and biological networks, pointing to possible interdisciplinary collaboration—e.g., application of biological network theories to refugee network dynamics—, potentially furthering theoretical development with respect to both network theory and theories on human mobility.

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Walton, N. S. "Utility Optimization in Congested Queueing Networks." Journal of Applied Probability 48, no. 1 (March 2011): 68–89. http://dx.doi.org/10.1239/jap/1300198137.

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We consider a multiclass single-server queueing network as a model of a packet switching network. The rates packets are sent into this network are controlled by queues which act as congestion windows. By considering a sequence of congestion controls, we analyse a sequence of stationary queueing networks. In this asymptotic regime, the service capacity of the network remains constant and the sequence of congestion controllers act to exploit the network's capacity by increasing the number of packets within the network. We show that the stationary throughput of routes on this sequence of networks converges to an allocation that maximises aggregate utility subject to the network's capacity constraints. To perform this analysis, we require that our utility functions satisfy an exponential concavity condition. This family of utilities includes weighted α-fair utilities for α > 1.

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Walton, N. S. "Utility Optimization in Congested Queueing Networks." Journal of Applied Probability 48, no. 01 (March 2011): 68–89. http://dx.doi.org/10.1017/s0021900200007646.

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We consider a multiclass single-server queueing network as a model of a packet switching network. The rates packets are sent into this network are controlled by queues which act as congestion windows. By considering a sequence of congestion controls, we analyse a sequence of stationary queueing networks. In this asymptotic regime, the service capacity of the network remains constant and the sequence of congestion controllers act to exploit the network's capacity by increasing the number of packets within the network. We show that the stationary throughput of routes on this sequence of networks converges to an allocation that maximises aggregate utility subject to the network's capacity constraints. To perform this analysis, we require that our utility functions satisfy an exponential concavity condition. This family of utilities includes weighted α-fair utilities for α > 1.

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Koam, Ali N. A., Ali Ahmad, and Ashfaq Ahmed Qummer. "On the Study of Reverse Degree-Based Topological Properties for the Third Type of p th Chain Hex-Derived Network." Journal of Mathematics 2021 (November 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/4540276.

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Vertices and edges are made from a network, with the degree of a vertex referring to the number of connected edges. The chance of every vertex possessing a given degree is represented by a network’s degree appropriation, which reveals important global network characteristics. Many fields, including sociology, public health, business, medicine, engineering, computer science, and basic sciences, use network theory. Logistical networks, gene regulatory networks, metabolic networks, social networks, and driven networks are some of the most significant networks. In physical, theoretical, and environmental chemistry, a topological index is a numerical value assigned to a molecular structure/network that is used for correlation analysis. Hexagonal networks of dimension t are used to build hex-derived networks, which have a wide range of applications in computer science, medicine, and engineering. For the third type of hex-derived networks, topological indices of reverse degree based are discussed in this study.

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Naranjo Orovio, Consuelo. "A network of networks." HAU: Journal of Ethnographic Theory 11, no. 1 (March 1, 2021): 52–66. http://dx.doi.org/10.1086/713372.

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Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons, and Brian McCaughan. "A network of networks." Journal of Health Organization and Management 31, no. 2 (April 10, 2017): 223–36. http://dx.doi.org/10.1108/jhom-07-2016-0146.

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Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author’s participation in network activities and discussions with the agency’s staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency’s implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of “centralised decentralisation”, co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid – the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a “network of networks” through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture “strategic hybrids”, and enhance the impact of network activities on health system reform. Social implications Network governance requires more from network members than participation in a single network, as it involves health service professionals and consumers in a multi-network dynamic. This dynamic requires deliberations and collaborations to be flexible, and it increasingly positions members as “strategic hybrids” – people who have moved on from singular taken-as-given stances and identities, towards hybrid positionings and flexible perspectives. Originality/value This paper is novel in that it identifies a critical feature of health service reform and large system transformation: network governance is empowered through the dynamic co-location of and collaboration among healthcare networks, particularly when complemented with “enabler” teams of people specialising in programme implementation and evaluation.

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Hepworth, H. Philip. "A network of networks." International Social Work 30, no. 1 (January 1987): 5–9. http://dx.doi.org/10.1177/002087288703000102.

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Fuengfusin, Ninnart, and Hakaru Tamukoh. "Network with Sub-Networks." Proceedings of International Conference on Artificial Life and Robotics 25 (January 13, 2020): 191–94. http://dx.doi.org/10.5954/icarob.2020.os20-2.

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Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (March 30, 2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM function is the most suitable function in predicting mathematics students’ performance because it has a higher correlation coefficient and a lower Performance value.

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Drotar, Istvan, Balazs Lukacs, and Miklós Kuczmann. "LoRaWAN Network Performance Test." Acta Technica Jaurinensis 13, no. 4 (August 11, 2020): 268–80. http://dx.doi.org/10.14513/actatechjaur.v13.n4.547.

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There are several types of wireless IoT (Internet of Things) networks based on the connection distance between two communicating devices. For covering wide areas, LPWAN (Low Power Wide Area) networks can provide a good solution. These networks provide big coverage and low power consumption. One of the most popular LPWAN network is LoRaWAN (Long Range Wide Area Network). LoRaWAN networks are ideal for sending infrequent, small messages through long distances. In this article the network’s capacity, coverage and energy consumption have been tested. These are the most important attributes when designing a LoRaWAN network, so it can satisfy the requirements of LPWAN networks.

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Wang, Fei-Yue, Liuqing Yang, Xiang Cheng, Shuangshuang Han, and Jian Yang. "Network softwarization and parallel networks: beyond software-defined networks." IEEE Network 30, no. 4 (July 2016): 60–65. http://dx.doi.org/10.1109/mnet.2016.7513865.

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Vishvakarma, Tanmya. "A Review of Modern Computer Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 368–76. http://dx.doi.org/10.22214/ijraset.2022.46637.

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Abstract: A computer network's principal role is to facilitate the transfer of digital information between computers. An interconnected network of computer equipment is all that constitutes a computer network. Distributing tasks among different machines is frequent in a computer network. Most networks confront substantial threats from attacks on their resources. This research focuses on computer networks. It is only through the establishment of a computer network that this is made possible at all. Nodes include things like computers, cell phones, servers, and other networking gear. Computer networks enable the Internet, as well as the shared use of application and storage servers, printers, and fax machines. Through the use of computer networks, many jobs can be completed. Systems can be classified as open or closed. If you want to connect to the network, the open system is ready to go. Closed systems, on the other hand, require further authentication to connect to other networks.

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Pathak, Sanjai, Ashish Mani, Mayank Sharma, and Amlan Chatterjee. "Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5 (May 17, 2023): 223–32. http://dx.doi.org/10.17762/ijritcc.v11i5.6609.

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The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks.

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WEN, HAO, ZHENG-FU HAN, GUANG-CAN GUO, and PEI-LIN HONG. "QKD NETWORKS WITH PASSIVE OPTICAL ELEMENTS: ANALYSIS AND ASSESSMENT." International Journal of Quantum Information 07, no. 06 (September 2009): 1217–31. http://dx.doi.org/10.1142/s0219749909005730.

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Quantum Key Distribution (QKD) networks are the trends toward multiple users' unconditional secure communication. Based on several passive optical devices, such as beam splitter, optical switch or wavelength divided multiplexer, various types of fiber-based QKD networks have been proposed. However, it is still hard to accurately assess these networks. To find the optimal solution, a general assessment that would not involve detailed schemes is quite necessary. In this paper, we introduce an evaluation method and analyze optical-device-based QKD networks including two rational aspects: (i) network connectivity and network bandwidth which reflect the network's flexibility and performance in theory; (ii) network cost that brings pragmatic restriction on the network construction in practice. Applying this model, we compare five typical types of optical-device-based QKD networks. The explicit results demonstrate the above networks' characteristics and some valuable conclusions.

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Bunimovich, L. A., D. C. Smith, and B. Z. Webb. "Specialization Models of Network Growth." Journal of Complex Networks 7, no. 3 (September 17, 2018): 375–92. http://dx.doi.org/10.1093/comnet/cny024.

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AbstractOne of the most important features observed in real networks is that, as a network’s topology evolves so does the network’s ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Herein, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe their growth. For example, we find that by randomly selecting these elements a network’s topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity and a right-skewed degree distribution. Beyond this, we show how this model can be used to generate networks with real-world like clustering coefficients and power-law degree distributions, respectively. As far as the authors know, this is the first such class of models that can create an increasingly modular and hierarchical network topology with these properties.

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Lynn, Christopher W., and Danielle S. Bassett. "Quantifying the compressibility of complex networks." Proceedings of the National Academy of Sciences 118, no. 32 (August 4, 2021): e2023473118. http://dx.doi.org/10.1073/pnas.2023473118.

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Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding—or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here, we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization—which is characterized by modular structure and heterogeneous degrees—facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network’s structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.

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Hari, Parli Baijnath, and Shailendra Narayan Singh. "Security Attacks at MAC and Network Layer in Wireless Sensor Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (December 20, 2019): 82–89. http://dx.doi.org/10.5373/jardcs/v11i12/20193215.

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Walker, David M., Antoinette Tordesillas, Amy L. Rechenmacher, and Michael Small. "Multiscale resolution of networks of granular media network evolution—a network of networks." IEICE Proceeding Series 2 (March 17, 2014): 294–97. http://dx.doi.org/10.15248/proc.2.294.

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Peng, Hao, Cheng Qian, Dandan Zhao, Ming Zhong, Jianmin Han, and Wei Wang. "Targeting attack hypergraph networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073121. http://dx.doi.org/10.1063/5.0090626.

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In modern systems, from brain neural networks to social group networks, pairwise interactions are not sufficient to express higher-order relationships. The smallest unit of their internal function is not composed of a single functional node but results from multiple functional nodes acting together. Therefore, researchers adopt the hypergraph to describe complex systems. The targeted attack on random hypergraph networks is still a problem worthy of study. This work puts forward a theoretical framework to analyze the robustness of random hypergraph networks under the background of a targeted attack on nodes with high or low hyperdegrees. We discovered the process of cascading failures and the giant connected cluster (GCC) of the hypergraph network under targeted attack by associating the simple mapping of the factor graph with the hypergraph and using percolation theory and generating function. On random hypergraph networks, we do Monte-Carlo simulations and find that the theoretical findings match the simulation results. Similarly, targeted attacks are more effective than random failures in disintegrating random hypergraph networks. The threshold of the hypergraph network grows as the probability of high hyperdegree nodes being deleted increases, indicating that the network’s resilience becomes more fragile. When considering real-world scenarios, our conclusions are validated by real-world hypergraph networks. These findings will help us understand the impact of the hypergraph’s underlying structure on network resilience.

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Cai, Hongyun, Xiaomei Gong, and Jianlei Han. "Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China." Academic Journal of Management and Social Sciences 2, no. 1 (March 27, 2023): 134–45. http://dx.doi.org/10.54097/ajmss.v2i1.6504.

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Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.

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Treur, Jan. "Analysis of a network’s asymptotic behavior via its structure involving its strongly connected components." Network Science 8, S1 (October 1, 2019): S82—S109. http://dx.doi.org/10.1017/nws.2019.24.

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AbstractIn this paper, it is addressed how network structure can be related to asymptotic network behavior. If such a relation is studied, that usually concerns only strongly connected networks and only linear functions describing the dynamics. In this paper, both conditions are generalized. A couple of general theorems is presented that relates asymptotic behavior of a network to the network’s structure characteristics. The network structure characteristics, on the one hand, concern the network’s strongly connected components and their mutual connections; this generalizes the condition of being strongly connected to a very general condition. On the other hand, the network structure characteristics considered generalize from linear functions to functions that are normalized, monotonic, and scalar-free, so that many nonlinear functions are also covered. Thus, the contributed theorems generalize the existing theorems on the relation between network structure and asymptotic network behavior addressing only specific cases such as acyclic networks, fully, and strongly connected networks, and theorems addressing only linear functions. This paper was invited as an extended (by more than 45%) version of a Complex Networks’18 conference paper. In the discussion section, the differences are explained in more detail.

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Singh, Preet Kamal, Harmeet Singh, and Jaspreet Kaur. "C4O: chain-based cooperative clustering using coati optimization algorithm in WSN." International Journal of Reconfigurable and Embedded Systems (IJRES) 13, no. 1 (March 1, 2024): 96. http://dx.doi.org/10.11591/ijres.v13.i1.pp96-104.

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In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.

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LANDSPERGER, JOHANNES, PATRICK SPIETH, and SVEN HEIDENREICH. "HOW NETWORK MANAGERS CONTRIBUTE TO INNOVATION NETWORK PERFORMANCE." International Journal of Innovation Management 16, no. 06 (November 30, 2012): 1240009. http://dx.doi.org/10.1142/s1363919612400099.

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Innovation networks that aim at the joint development of products, services or processes represent a particular form of inter-organizational business networks. In order to yield useful results from these collaborations, networks need to be managed thoroughly. By appointing a dedicated network manager to administrate, coordinate, and regulate, the management of tasks is bundled and centralized within a single entity. However, to the best knowledge of the authors, no empirical research has yet been conducted, investigating the impact of a network manager's availability, relevance, and influence on network performance. Using the interaction-oriented network approach as conceptual foundation, we analyze network managers' direct and indirect influence on the network's relational and goal achievement performance. Our results suggest that a network manager enhances innovation network's core management functions, which in turn improve the relational performance (RP). Moreover, RP was found to significantly drive the goal achievement performance (GAP).

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Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (March 2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.

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The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provide large coverage to achieve load balancing. By combing Generative Adversarial Network (GAN) with the ns-3 network simulator, this paper uses neural networks in TensorFlow to optimize load balancing of mobile networks, increase the data throughput and reduce the packet loss rate. In addition, to discuss the load balancing problem, we take the data produced by the ns-3 network simulator as the real data for GAN.

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Boroojeni, Asma Azizi, Jeremy Dewar, Tong Wu, and James M. Hyman. "Generating bipartite networks with a prescribed joint degree distribution." Journal of Complex Networks 5, no. 6 (June 27, 2017): 839–57. http://dx.doi.org/10.1093/comnet/cnx014.

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Abstract We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree–degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor–movie networks, author–article networks, co-occurrence networks and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbours of that node that must be preserved when generating a network that reflects the structure of the underling system. Our bipartite $2K$ ($B2K$) algorithms generate an ensemble of networks that preserve prescribed degree sequences for the two disjoint set of nodes in the bipartite network, and the joint-degree distribution that is the distribution of the degrees of all neighbours of nodes with the same degree. We illustrate the effectiveness of the algorithms on a romance network using the NetworkX software environment to compare other properties of a target network that are not directly enforced by the $B2K$ algorithms. We observe that when average degree of nodes is low, as is the case for romance and heterosexual partnership networks, then the $B2K$ networks tend to preserve additional properties, such as the cluster coefficients, than algorithms that do not preserve the joint-degree distribution of the original network.

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Sharma, Anita, and Prashant Salwan. "Network Matters! Revisiting Social Networks." Academy of Management Proceedings 2017, no. 1 (August 2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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Fuengfusin, Ninnart, and Hakaru Tamukoh. "Convolutional Network with Sub-Networks." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 6–9. http://dx.doi.org/10.5954/icarob.2021.os19-1.

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Hampson, J. "Urban network development [distribution networks]." Power Engineering Journal 15, no. 5 (October 1, 2001): 224–32. http://dx.doi.org/10.1049/pe:20010504.

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Hirai, Shigeoki. "Multimedia Networks. Network and Telerobotics." Journal of the Robotics Society of Japan 15, no. 4 (1997): 516–19. http://dx.doi.org/10.7210/jrsj.15.516.

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Tizghadam, Ali, Weiwei Li, and Alberto Leon-Garcia. "Network criticality in vehicular networks." ACM SIGMETRICS Performance Evaluation Review 40, no. 3 (December 4, 2012): 107–9. http://dx.doi.org/10.1145/2425248.2425278.

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Jiang, Chunxiao, Yan Chen, K. J. Ray Liu, and Yong Ren. "Network economics in cognitive networks." IEEE Communications Magazine 53, no. 5 (May 2015): 75–81. http://dx.doi.org/10.1109/mcom.2015.7105644.

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Kilkenny, Maureen, and Nerys Fuller Love. "Network analysis and business networks." International Journal of Entrepreneurship and Small Business 21, no. 3 (2014): 303. http://dx.doi.org/10.1504/ijesb.2014.060894.

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Havlin, S., D. Y. Kenett, A. Bashan, J. Gao, and H. E. Stanley. "Vulnerability of network of networks." European Physical Journal Special Topics 223, no. 11 (October 2014): 2087–106. http://dx.doi.org/10.1140/epjst/e2014-02251-6.

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Rutherford, Ian. "Network Theory and Theoric Networks." Mediterranean Historical Review 22, no. 1 (June 2007): 23–37. http://dx.doi.org/10.1080/09518960701538523.

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Wang, Shijun, Zhongbao Kou, and Changshui Zhang. "Network boosting on different networks." Physica A: Statistical Mechanics and its Applications 366 (July 2006): 561–70. http://dx.doi.org/10.1016/j.physa.2005.10.023.

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Huang, Gang, and Mark Tausig. "Network range in personal networks." Social Networks 12, no. 3 (September 1990): 261–68. http://dx.doi.org/10.1016/0378-8733(90)90009-x.

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Stalker, James, John Lasley, George Frederick, Renee McPherson, Paul Campbell, Brenda Philips, and Bob Pasken. "A Nationwide Network of Networks." Bulletin of the American Meteorological Society 94, no. 10 (October 1, 2013): 1602–6. http://dx.doi.org/10.1175/1520-0477-94.10.1602.

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Hobbs, Linn W. "Network topology in aperiodic networks." Journal of Non-Crystalline Solids 192-193 (December 1995): 79–91. http://dx.doi.org/10.1016/0022-3093(95)00431-9.

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Journal articles on the topic 'Networks'

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