Статті в журналах: "Network analysis"

1

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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Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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3

CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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4

KalaiSelvi, Dr B., and Aruna K. "Network Traffic Analysis Using Wireshark." International Journal of Research Publication and Reviews 4, no. 12 (December 18, 2023): 1960–65. http://dx.doi.org/10.55248/gengpi.4.1223.123506.

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5

Blair, Alan D., and Jordan B. Pollack. "Analysis of Dynamical Recognizers." Neural Computation 9, no. 5 (July 1, 1997): 1127–42. http://dx.doi.org/10.1162/neco.1997.9.5.1127.

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Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and interacted function system-like fractal state sets. Follow on work focused mainly on the extraction and minimization of a finite state automaton (FSA) from the trained network. However, such networks are capable of inducing languages that are not regular and therefore not equivalent to any FSA. Indeed, it may be simpler for a small network to fit its training data by inducing such a nonregular language. But when is the network's language not regular? In this article, using a low-dimensional network capable of learning all the Tomita data sets, we present an empirical method for testing whether the language induced by the network is regular. We also provide a detailed "-machine analysis of trained networks for both regular and nonregular languages.

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6

Karimi, Faezeh, David Green, Petr Matous, Manos Varvarigos, and Kaveh R. Khalilpour. "Network of networks: A bibliometric analysis." Physica D: Nonlinear Phenomena 421 (July 2021): 132889. http://dx.doi.org/10.1016/j.physd.2021.132889.

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7

Lobastova, M., A. Matyukhin, and A. Muthanna. "Analysis of Network Reliability of Network Synchronization." Telecom IT 8, no. 4 (December 23, 2020): 93–99. http://dx.doi.org/10.31854/2307-1303-2020-8-4-93-99.

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This article describes the challenges of modern communication networks reliability, analyses ITU-T recommendations and regulations governing the communication networks reliability in Russian Federation. The network clock network is an integral part of digital communication networks. Therefore, the issue of the synchronization network reliability should be given great attention. Research subject. In this article, we discussed the reliability of the clock synchronization network. Method. The main mathematical tools are graph theory and probability theory. To implement the proposed method for assessing the structural reliability of the synchronization network, the direct search method is used. Core results. The results allow us to conclude that the proposed method can be applied to assess the structural reliability of the clock synchronization network. Practical relevance. The solution proposed in this article can be used for a reasonable assessment of the network structural reliability indicators, which is necessary for making a decision on the choice of a route for transmitting a synchronization signal.

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8

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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9

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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10

Hafner-Burton, Emilie M., Miles Kahler, and Alexander H. Montgomery. "Network Analysis for International Relations." International Organization 63, no. 3 (July 2009): 559–92. http://dx.doi.org/10.1017/s0020818309090195.

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International relations research has regarded networks as a particular mode of organization, distinguished from markets or state hierarchies. In contrast, network analysis permits the investigation and measurement of network structures—emergent properties of persistent patterns of relations among agents that can define, enable, and constrain those agents. Network analysis offers both a toolkit for identifying and measuring the structural properties of networks and a set of theories, typically drawn from contexts outside international relations, that relate structures to outcomes. Network analysis challenges conventional views of power in international relations by defining network power in three different ways: access, brokerage, and exit options. Two issues are particularly important to international relations: the ability of actors to increase their power by enhancing and exploiting their network positions, and the fungibility of network power. The value of network analysis in international relations has been demonstrated in precise description of international networks, investigation of network effects on key international outcomes, testing of existing network theory in the context of international relations, and development of new sources of data. Partial or faulty incorporation of network analysis, however, risks trivial conclusions, unproven assertions, and measures without meaning. A three-part agenda is proposed for future application of network analysis to international relations: import the toolkit to deepen research on international networks; test existing network theories in the domain of international relations; and test international relations theories using the tools of network analysis.

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11

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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12

Quinn, Darren, Liming Chen, and Maurice Mulvenna. "Social Network Analysis." International Journal of Ambient Computing and Intelligence 4, no. 3 (July 2012): 46–58. http://dx.doi.org/10.4018/jaci.2012070104.

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Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.

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13

Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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14

Löblich, Maria, and Senta Pfaff-Rüdiger. "Network analysis." International Communication Gazette 73, no. 7 (November 2011): 630–47. http://dx.doi.org/10.1177/1748048511417159.

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Structures of interest, influence and power in communication policy are shifting due to increasing digitization, economization and globalization. Against this background it is necessary to reflect more than before upon methods to study the relations between actors and their potential influences on regulation processes. The article explains qualitative network analysis as a research strategy for communication policy research and discusses qualitative interviews and network cards as research tools to put network analysis into practice. The theoretical basis consists of governance and network theory. The article focuses on an analysis of the German youth protection system, which provides practical insight into a qualitative network study.

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15

Mills, E. J., K. Thorlund, and J. P. A. Ioannidis. "Demystifying trial networks and network meta-analysis." BMJ 346, may14 2 (May 14, 2013): f2914. http://dx.doi.org/10.1136/bmj.f2914.

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16

Davel, Ronel, Adeline S. A. Du Toit, and Martie M. Mearns. "Understanding Knowledge Networks Through Social Network Analysis." International Journal of Knowledge Management 13, no. 2 (April 2017): 1–17. http://dx.doi.org/10.4018/ijkm.2017040101.

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Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour to illustrate via this question that cultivating synergies between SNA, CoPs and knowledge maps will enable organisations to produce stronger knowledge networks and ultimately increase their social capital. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.

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17

Robins, Garry, Jenny M. Lewis, and Peng Wang. "Statistical Network Analysis for Analyzing Policy Networks." Policy Studies Journal 40, no. 3 (August 2012): 375–401. http://dx.doi.org/10.1111/j.1541-0072.2012.00458.x.

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18

Fath, Brian D., Ursula M. Scharler, Robert E. Ulanowicz, and Bruce Hannon. "Ecological network analysis: network construction." Ecological Modelling 208, no. 1 (October 2007): 49–55. http://dx.doi.org/10.1016/j.ecolmodel.2007.04.029.

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19

Fang, Yimeng. "Robustness analysis of highway network based on complex network." Highlights in Science, Engineering and Technology 42 (April 7, 2023): 291–97. http://dx.doi.org/10.54097/hset.v42i.7108.

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Taking Chongqing, Hunan, Shandong, Shaanxi and Sichuan as examples, this paper conducts a comparative study on the robustness of their highway networks, which is helpful for the subsequent construction of China's highway networks. The topology structure of highway networks is studied by complex network theory. The degree distribution, average degree, average clustering coefficient, average path length, network diameter and other parameters of the network were calculated, and the robustness of the highway network in five provinces and cities was compared from four aspects: connectivity, network efficiency, turn rate and robustness r. The results show that Chongqing and Shaanxi have a good performance of highway network robustness, Shandong and Sichuan have a more balanced performance, and Hunan has a weak performance. Enhance network robustness by placing route planning directions in place with fewer route options to provide drivers with more route options.

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20

Liu, Yaoxuan. "Analysis of network resilienceon global air transportation." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 67–75. http://dx.doi.org/10.54254/2755-2721/6/20230678.

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Small-world network is a very common network structure, which is characterized by low average degree, small average path length and high centrality. At the same time, small-world networks have high resilience to random errors and low resilience to targeted attacks. In this study, the importance of nodes is represented by attributes such as degree and centrality, and attacks refer to the removal of important nodes. The network is attacked according to the degree, betweenness and closeness centrality to observe the power distribution. The data is mainly obtained from the open source OpenFlight. Gephi, Python, and Excel are used as tools. Gephi is used for network visualization and analysis. The third-party python libraries Pandas, Matplotlib, and NetworkX were used in this study to deal with the things that Gephi can't compute or represent well, and then plot the corresponding graphs with Matplotlib. The work of cleaning data is mainly done by excel.

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21

KOVACIK, Cyril Filip, and Gabriel BUGAR. "ANALYSIS OF OPERATIONAL PROPERTIES OF VOIP NETWORK." Acta Electrotechnica et Informatica 1335-8243, no. 1338-3957 (June 9, 2021): 30–34. http://dx.doi.org/10.15546/aeei-2021-0005.

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Voice transmission over the Internet network is now taken for granted. Many end-user applications address this issue. However, this paper focuses on the specific use of the SCCP protocol created by Cisco, its implementation in a computer network and end devices, determination of the operational properties of this implementation, and their comparison in different conditions. VoIP traffic is compared at different bandwidths and implemented by different configurations of IP protocols. By investigated implementations of IP protocols are meant IPv4, IPv6, and IPv4 protocol with applied NAT. As part of the application of various IP protocols is also compared VoIP communication with a video stream on a local basis. The conclusion of the paper is devoted to the graphical evaluation of these observations and to draw conclusions based on them.

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22

Kamiyama, Noriaki. "DESIGNING NETWORK TOPOLOGY USING DATA ENVELOPMENT ANALYSIS." Journal of the Operations Research Society of Japan 56, no. 3 (2013): 199–220. http://dx.doi.org/10.15807/jorsj.56.199.

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23

Chen, Wei-Yu, Shing-Han Li, Mann-Jung Hsiao, Chung-Chiang Hu, and Kuo-Ching Tu. "Network Security Analysis by Using Business Intelligence." International Journal of Machine Learning and Computing 5, no. 6 (December 2015): 431–38. http://dx.doi.org/10.18178/ijmlc.2015.5.6.547.

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24

Ganesh, V. Dilli, and G. Vadivu. "Ego Network Analysis for Predicting Students Performance." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 722–35. http://dx.doi.org/10.31142/ijtsrd2364.

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25

Shcherbakov, V. S., and I. A. Karpov. "Regional Inflation Analysis Using Social Network Data." Economy of Regions 20, no. 3 (2024): 930–46. http://dx.doi.org/10.17059/ekon.reg.2024-3-21.

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Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by a range of factors, including inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of the Internet, especially social networks. It is hypothesised that people search, read, and discuss mainly only those issues that are of particular interest to them. It is logical to assume that the dynamics of prices may also be in the focus of users’ discussions. So, such discussions could be regarded as an alternative source of more rapid information about inflation expectations. This study is based on unstructured data from VKontakte social network used to analyse upward and downward inflationary trends (on the example of the Omsk region). The sample of more than 8.5 million posts was collected between January 2010 and May 2022. The authors used BERT neural networks to solve the problem. These models demonstrated better results than the benchmarks (e.g., logistic regression, decision tree classifier, etc.). It makes possible to define pro-inflationary and disinflationary types of keywords in different contexts and get their visualisation with SHAP method. This analysis provides additional operational information about inflationary processes at the regional level The proposed approach can be scaled for other regions. At the same time, the limitation of the work is the time and power costs for the initial training of similar models for all regions of Russia.

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Tran, Ngoc Tam L., Luke DeLuccia, Aidan F. McDonald, and Chun-Hsi Huang. "Cross-Disciplinary Detection and Analysis of Network Motifs." Bioinformatics and Biology Insights 9 (January 2015): BBI.S23619. http://dx.doi.org/10.4137/bbi.s23619.

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The detection of network motifs has recently become an important part of network analysis across all disciplines. In this work, we detected and analyzed network motifs from undirected and directed networks of several different disciplines, including biological network, social network, ecological network, as well as other networks such as airlines, power grid, and co-purchase of political books networks. Our analysis revealed that undirected networks are similar at the basic three and four nodes, while the analysis of directed networks revealed the distinction between networks of different disciplines. The study showed that larger motifs contained the three-node motif as a subgraph. Topological analysis revealed that similar networks have similar small motifs, but as the motif size increases, differences arise. Pearson correlation coefficient showed strong positive relationship between some undirected networks but inverse relationship between some directed networks. The study suggests that the three-node motif is a building block of larger motifs. It also suggests that undirected networks share similar low-level structures. Moreover, similar networks share similar small motifs, but larger motifs define the unique structure of individuals. Pearson correlation coefficient suggests that protein structure networks, dolphin social network, and co-authorships in network science belong to a superfamily. In addition, yeast protein-protein interaction network, primary school contact network, Zachary's karate club network, and co-purchase of political books network can be classified into a superfamily.

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Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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28

KC, Birendra, Duarte B. Morais, M. Nils Peterson, Erin Seekamp, and Jordan W. Smith. "Social network analysis of wildlife tourism microentrepreneurial network." Tourism and Hospitality Research 19, no. 2 (June 30, 2017): 158–69. http://dx.doi.org/10.1177/1467358417715679.

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Social networks are an important element of entrepreneurship. Entrepreneurs rely on social networks to access ideas, information, and resources to facilitate their entrepreneurial process. Strong and weak ties influence the entrepreneurial process in unique ways. This study utilized social network analysis approach to examine wildlife tourism microentrepreneurship through in-person structured interviews with 37 microentrepreneurs from North Carolina’s Pamlico Sound Region. Specifically, this study examined the extent of network ties, the type of support received from those network ties, and the process of creating and maintaining the business network ties. Weak ties were more prevalent than strong ties. Support was received in terms of marketing and advertising, information sharing, and product sponsorship. Weak ties were established through professional workshops and seminars or while working in the same territory, whereas reciprocity, togetherness, communication, and trust were identified as major factors to maintain weak ties. This study suggests that cognitive social capital factors (e.g. reciprocity, togetherness, and trust) can be highly important toward effective use of social networks, as well as to ensure entrepreneurial success.

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AYDIN, Nursen. "Social Network Analysis: Literature Review." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 73–80. http://dx.doi.org/10.5824/1309-1581.2018.4.005.x.

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In this article, social network analysis SNA is defined and historical development process is explained. A comprehensive literature search has been conducted for this purpose. SAA is a powerful method that centralizes individuals and their relations, in that the effect of the individual on the social network can be uncovered and the network of individual groups can be evaluated holistically. SNA shows the structural gaps and social capital in institutions, and focuses managers' attention on critical informal networks. Evaluating strategically important networks within an organization, make "invisible" groups visible in the interaction and allows them to work with key groups to facilitate effective collaboration.

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ELAGIN, V. S., and A. S. VASIN. "ANALYSIS OF NETWORK RESOURCE SCALING MODELS IN 5G NETWORK." T-Comm 17, no. 5 (2023): 32–41. http://dx.doi.org/10.36724/2072-8735-2023-17-5-32-41.

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This article analyzes the existing models of virtual network embedding and dynamic scaling of virtual network resources for Network Slices. These models make it possible to provide services with the required QoS while respecting the concept of efficient network resource usage. Dynamic virtual network embedding models allow virtual networks to be efficiently mapped on a substrate network and reconfigured on demand. But for more flexible dynamic scaling, Holt-Winters, Bi-LSTM and etc. models are additionally used, which are built according algorithms for predicting future resource utilization in order to reduce the initialization time of virtual network function instances serving certain Network Slices. Dynamic scaling models are compared and conclusions about the possibility of their use are given. As a conclusion we were made a summary about the possibility of using these models and the necessity of improvements for more flexible use in 5G networks.

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Zhang, Haomiao, and Qing Liu. "Optimization Algorithm of Communication Resource Allocation in a Complex Network Based on an Improved Neural Network." Journal of Function Spaces 2022 (July 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/7309612.

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The traditional optimization algorithm of communication resource allocation in a complex network has the disadvantage of weak antijamming ability, and the communication quality decreases obviously when the number of users is large. In China’s large urban network applications, mobile phones and other networks can have problems such as reduced network efficiency when there are more access users at some communication base stations, thus affecting user network usage. An optimization algorithm of communication resource allocation in the complex network based on an improved neural network is proposed. Increase inertia improves the traditional BP neural network algorithm, using the average path length, clustering coefficient, and connectivity distribution index analysis of the complex network; the improved Hopfield neural network is utilized to confirm each user volume size; it is concluded that their users are able to get the number of subchannels, through the instantaneous channel coarse pair gain dynamic channel allocation, calculating bit load matrix at the same time, minimize transmission power, and achieve bit loading and power allocation and communication resource allocation optimization. Experimental results show that the proposed method has better application performance by introducing the improved neural network and suppressing the external interference on the basis of enhancing the communication effect.

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Scherbakova, N. G., and S. V. Bredikhin. "Co-authorship network structure analysis." Journal of Physics: Conference Series 2099, no. 1 (November 1, 2021): 012055. http://dx.doi.org/10.1088/1742-6596/2099/1/012055.

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Abstract The analysis of networks of collaboration between scientists reveals features of academic communities that help in understanding the specifics of collaborative scientific work and identifying the notable researchers. In these networks, the set of nodes consists of authors and there exists a link between two authors if they have coauthored one or more papers. This article presents an analysis of the co-authorship network based on bibliometric data retrieved from the distributed economic database. Here we use the simple network model without taking into account the strength of collaborative ties. The data were analyzed using statistical techniques in order to get such parameters as the number of papers per author, the number of authors per paper, the average number of coauthors per author and collaboration indices. We show that the largest component occupies near 90 % of the network and the node degree distribution follows a power-law. The study of typical distances between nodes and the degree of clustering makes it possible to classify the network as a ‘small world’ network.

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Khan, Faiz M., Shailendra K. Gupta, and Olaf Wolkenhauer. "Integrative workflows for network analysis." Essays in Biochemistry 62, no. 4 (October 26, 2018): 549–61. http://dx.doi.org/10.1042/ebc20180005.

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Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus–response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial–mesenchymal transition from the E2F1 molecular interaction map. In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.

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Arul, Sharmila Mary, Gowri Senthil, S. Jayasudha, Ahmed Alkhayyat, Khalikov Azam, and R. Elangovan. "Graph Theory and Algorithms for Network Analysis." E3S Web of Conferences 399 (2023): 08002. http://dx.doi.org/10.1051/e3sconf/202339908002.

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In network analysis, the study and comprehension of complex systems in numerous fields, such as social networks, transportation networks, and biological networks, are made possible by the crucial role played by graph theory and algorithms. In order to give a comprehensive review of the graph theory and network analysis methods, this abstract will focus on their significance, practical uses, and most recent developments. With items represented as nodes or vertices and links between them as edges, graph theory offers a mathematical framework for modeling and evaluating relationships between objects. Researchers may learn important things about the structure, connectivity, and behavior of complex systems by using graph theory in network analysis. As a result, network analysis is made possible by the graph theory and algorithms, which offer strong tools for studying and comprehending the complicated linkages and structures of complex systems. Graph theory and algorithms have many different applications, including social networks, transportation networks, and biological networks. Large-scale network analysis is now possible thanks to the development of effective algorithms and methodologies, which has significantly advanced the subject. The significance of graph theory and algorithms for network research will only rise as networks continue to expand in size and complexity.

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35

Mishra, Saurabh, Prof Rakesh Ranjan, Dr Sonika Singh, and Dr Gagan Singh. "Performance Analysis of MIMO Heterogeneous Wireless Sensor Networks." International Journal of Innovative Technology and Exploring Engineering 12, no. 12 (November 30, 2023): 25–31. http://dx.doi.org/10.35940/ijitee.l9742.11121223.

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Wireless Sensor Networks (WSN) are widely used in remote applications related to defence and healthcare. A network with nodes having different capabilities like sensing, various computational capabilities, power-efficient communication, and a varied sensing range is called a heterogeneous wireless sensor network. Heterogeneous wireless sensor networks using MIMO wireless channels are more useful for energy-efficient multi-channel communication. MIMO applications in wireless sensor networks have the potential to enhance throughput, reduce End-to-End Delay, improve packet delivery ratios, and conserve energy in wireless sensor networks. Its implementation needs to be carefully considered in light of the specific deployment conditions and resource constraints of the network, considering proper antenna design, synchronisation mechanisms, and energy-efficient algorithms. This paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO for various Quality of Service parameters like Packet Delivery Ratio, End to End Delay, Throughput and Residual energy. The research work shows that the application of MIMO in Wireless Sensor Networks enables sensor nodes to collaborate effectively, leading to improved reliability and coverage, and also increases the network's lifetime by conserving energy in resource-constrained sensor nodes through the preservation of Residual Energy.

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36

Oesterheld, Matthias, Hans Werner Mewes, and Volker Stümpflen. "Analysis of integrated biomolecular networks using a generic network analysis suite." Journal of Integrative Bioinformatics 4, no. 3 (December 1, 2007): 147–57. http://dx.doi.org/10.1515/jib-2007-72.

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Abstract The informative value of biomolecular networks has shifted from being solely information resources for possible cellular partners (whether these embody proteins, (ribo)nucleic acids or small molecules) towards becoming models for the functional connectivity within a cell. These models are increasingly exploited to make quantitative predictions about the cell’s functional organization as well as about the functionality of individual elements in the network.A large number of concepts and methods have been proposed in order to interpret experimental data mapped to cellular networks these systems and to make use of the rich source of information they represent.We will present a system for the Comprehensive Analysis of Biomolecular Networks (CABiNet), capable of integrating available network analysis methods. Integration is done by classifying each method into one of four separate categories using standardized interfaces that encapsulate the functionality of the method in a distinct component with standardized in- and output. These components can be accessed individually or in an integrated form using a processing pipeline for semi-automatic analyses.Additionally, the system can be used to query both biomolecular networks as well as the derived results of network analysis methods, such as clustering algorithms, in order to provide a service for researchers who are focused towards the functional context of any particular cellular entity.CABiNet is designed in an easy-to-use and easy-to-extend software framework that allows a straightforward integration of novel components. We will demonstrate the capabilities of the system by introducing several use cases.The CABiNet suite can be accessed at http://mips.gsf.de/genre/proj/CABiNet. Source code including additional components that can be accessed using the API is available upon request.

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37

Eremeev, Igor, Maxim Tatarka, Fedor Shuvaev, and Andrey Tsyganov. "Comparative Analysis of Centrality Measures of Network Nodes based on Principal Component Analysis." Informatics and Automation 19, no. 6 (December 11, 2020): 1307–31. http://dx.doi.org/10.15622/ia.2020.19.6.7.

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. The analysis of networks of a diverse nature, which are citation networks, social networks or information and communication networks, includes the study of topological properties that allow one to assess the relationships between network nodes and evaluate various characteristics, such as the density and diameter of the network, related subgroups of nodes, etc. For this, the network is represented as a graph – a set of vertices and edges between them. One of the most important tasks of network analysis is to estimate the significance of a node (or in terms of graph theory – a vertex). For this, various measures of centrality have been developed, which make it possible to assess the degree of significance of the nodes of the network graph in the structure of the network under consideration. The existing variety of measures of centrality gives rise to the problem of choosing the one that most fully describes the significance and centrality of the node. The relevance of the work is due to the need to analyze the centrality measures to determine the significance of vertices, which is one of the main tasks of studying networks (graphs) in practical applications. The study made it possible, using the principal component method, to identify collinear measures of centrality, which can be further excluded both to reduce the computational complexity of calculations, which is especially important for networks that include a large number of nodes, and to increase the reliability of the interpretation of the results obtained when evaluating the significance node within the analyzed network in solving practical problems. In the course of the study, the patterns of representation of various measures of centrality in the space of principal components were revealed, which allow them to be classified in terms of the proximity of the images of network nodes formed in the space determined by the measures of centrality used.

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38

Parise, Francesca, and Asuman Ozdaglar. "Analysis and Interventions in Large Network Games." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 455–86. http://dx.doi.org/10.1146/annurev-control-072020-084434.

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We review classic results and recent progress on equilibrium analysis, dynamics, and optimal interventions in network games with both continuous and discrete strategy sets. We study strategic interactions in deterministic networks as well as networks generated from a stochastic network formation model. For the former case, we review a unifying framework for analysis based on the theory of variational inequalities. For the latter case, we highlight how knowledge of the stochastic network formation model can be used by a central planner to design interventions for large networks in a computationally efficient manner when exact network data are not available.

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39

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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40

Song, Yuning, Liping Ding, Mengying Dong, Xuehua Liu, and Xiao Wang. "Multi-mode Network Analysis under Differential Privacy." Journal of Physics: Conference Series 2082, no. 1 (November 1, 2021): 012010. http://dx.doi.org/10.1088/1742-6596/2082/1/012010.

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Abstract With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.

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41

Skvoretz, John. "Pas de Deux: Social Networks and Network Analysis." Contemporary Sociology: A Journal of Reviews 37, no. 5 (September 2008): 423–26. http://dx.doi.org/10.1177/009430610803700511.

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42

Kyriakopoulos, F., S. Thurner, C. Puhr, and S. W. Schmitz. "Network and eigenvalue analysis of financial transaction networks." European Physical Journal B 71, no. 4 (July 21, 2009): 523–31. http://dx.doi.org/10.1140/epjb/e2009-00255-7.

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43

Pe'er, D. "Bayesian Network Analysis of Signaling Networks: A Primer." Science Signaling 2005, no. 281 (April 19, 2005): pl4. http://dx.doi.org/10.1126/stke.2812005pl4.

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Chan, Wai Kin Victor, and Cheng Hsu. "How Hyper-Network Analysis Helps Understand Human Networks?" Service Science 2, no. 4 (December 2010): 270–80. http://dx.doi.org/10.1287/serv.2.4.270.

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Himelboim, Itai, Marc A. Smith, Lee Rainie, Ben Shneiderman, and Camila Espina. "Classifying Twitter Topic-Networks Using Social Network Analysis." Social Media + Society 3, no. 1 (January 2017): 205630511769154. http://dx.doi.org/10.1177/2056305117691545.

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As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.

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46

Cárdenas, Julián. "Varieties of corporate networks: Network analysis and fsQCA." International Journal of Comparative Sociology 53, no. 4 (August 2012): 298–322. http://dx.doi.org/10.1177/0020715212460257.

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Monteiro, R. P., A. M. A. Garcia, and C. J. A. Bastos Filho. "Structural Analysis of Road Networks Using Network Science." IEEE Latin America Transactions 14, no. 10 (October 2016): 4386–91. http://dx.doi.org/10.1109/tla.2016.7786320.

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Schwartz, Daniel M., and Tony (D.A.) Rouselle. "Using social network analysis to target criminal networks." Trends in Organized Crime 12, no. 2 (October 24, 2008): 188–207. http://dx.doi.org/10.1007/s12117-008-9046-9.

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Rücker, Gerta. "Network meta-analysis, electrical networks and graph theory." Research Synthesis Methods 3, no. 4 (September 25, 2012): 312–24. http://dx.doi.org/10.1002/jrsm.1058.

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Nie, Chun-Xiao. "Hurst analysis of dynamic networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 2 (February 2022): 023130. http://dx.doi.org/10.1063/5.0070170.

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The sequence of network snapshots with time stamps is an effective tool for describing system dynamics. First, this article constructs a multifractal analysis of a snapshot network, in which the Hurst integral is used to describe the fractal structure hidden in structural dynamics. Second, we adjusted the network model and conducted comparative analysis to clarify the meaning of the Hurst exponent and found that the snapshot network usually includes multiple fractal structures, such as local and global fractal structures. Finally, we discussed the fractal structure of two real network datasets. We found that the real snapshot network also includes rich dynamics, which can be distinguished by the Hurst exponent. In particular, the dynamics of financial networks includes multifractal structures. This article provides a perspective to study the dynamic networks, thereby indirectly describing the fractal characteristics of complex system dynamics.

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