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Journal articles on the topic 'Modeling and network analysis'
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Pandey, Hemant, and Romi Bala. "Mathematical Approaches to Network Science: Modeling and Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2020): 1668–73. http://dx.doi.org/10.61841/turcomat.v11i1.14629.
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Network science, a multidisciplinary field, employs mathematical approaches to model and analyze complex systems as networks or graphs. This paper provides an overview of the fundamental concepts, mathematical modeling techniques, analysis methods, and applications of network science. It emphasizes the importance of mathematical approaches in understanding the structure and dynamics of networks in various domains, including social, biological, and technological networks. The paper also discusses challenges such as scalability and incorporating dynamics, along with future research directions. Overall, mathematical approaches are essential for advancing network science and unlocking new insights into complex systems.
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PÜTSCH, FELIX. "ANALYSIS AND MODELING OF SCIENCE COLLABORATION NETWORKS." Advances in Complex Systems 06, no. 04 (2003): 477–85. http://dx.doi.org/10.1142/s0219525903001043.
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We analyze a science collaboration network, i.e. a network whose nodes are scientists with edges connecting them for each paper published together. Furthermore, we develop a model for the simulation of discontiguous small-world networks that shows good coherence with empirical data.
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LIU, BING, and P. S. THIAGARAJAN. "MODELING AND ANALYSIS OF BIOPATHWAYS DYNAMICS." Journal of Bioinformatics and Computational Biology 10, no. 04 (2012): 1231001. http://dx.doi.org/10.1142/s0219720012310014.
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Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.
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Jang, Seung Gyeong, and Hae-Deok Song. "Keyword Network and Topic Modeling Analysis of Nurse Safety Education Literature." Korean Educational Research Association 63, no. 3 (2025): 31–52. https://doi.org/10.30916/kera.63.3.31.
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의료 환경에서 환자 안전을 위한 간호사의 역할이 더욱 중요해지고 있으며, 이에 따라 효과적인 교육 전략 개발이 필수적이다. 본 연구는 키워드 네트워크 분석을 활용하여 간호사 대상 환자 안전 교육 연구의 주요 개념 및 연구 흐름을 분석하고, 교육학적 시사점을 도출하는 것을 목적으로 한다. 2010년부터 2022년까지 Web of Science, PubMed, Scopus 데이터베이스에서 간호사 안전 교육과 관련된 총 1,133건의 문헌을 검색하였으며, 최종적으로 130건을 분석 대상으로 선정하였다. 연구 분석을 위해 R 프로그램을 활용하여 문헌의 제목, 초록, 키워드를 추출하였으며, 키워드의 빈도 및 중심성을 계산하고 워드클라우드 패키지를 사용하여 시각화하였다. 또한, 키워드 네트워크 분석을 적용하여 연구 주제 간 관계를 분석하고, 네트워크 중심성 및 군집 분석을 수행하였다. 분석 결과, 시뮬레이션, 팀, 실습, 지식, 술기 등의 키워드가 주요 개념으로 도출되었다. 또한, 전문직 간 협업 교육, 간호술기 향상 훈련, 시뮬레이션 기반 사례 교육과 관련된 연구 주제가 주요 토픽으로 도출되었다. 본 연구는 간호사 대상 환자 안전 교육이 단순한 지식 전달을 넘어, 실무 중심의 체험형 학습과 협업 역량 강화의 중요성을 강조하고 있음을 시사한다. 본 연구는 간호 교육에서 효과적인 교수법 개발을 위한 기초 자료를 제공할 것으로 기대된다.
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Mimouni, Faycal, and Abdellah Abouabdellah. "Proposition of a modeling and an analysis methodology of integrated reverse logistics chain in the direct chain." Journal of Industrial Engineering and Management 9, no. 2 (2016): 359. http://dx.doi.org/10.3926/jiem.1720.
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Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.Design/methodology/approach: Network modeling by combining Petri and Bayesian network.Findings: Modeling with Bayesian network complimented with Petri network to break the cycle problem in the Bayesian network.Research limitations/implications: Demands are independent from returns.Practical implications: Model can only be used on nonperishable products.Social implications: Legislation aspects: Recycling laws; Protection of environment; Client satisfaction via after sale service.Originality/value: Bayesian network with a cycle combined with the Petri Network.
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Wu, Weiqiang, Ning Huang, and Zhitao Wu. "Traffic chaotic dynamics modeling and analysis of deterministic network." Modern Physics Letters B 30, no. 18 (2016): 1650285. http://dx.doi.org/10.1142/s0217984916502857.
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Network traffic is an important and direct acting factor of network reliability and performance. To understand the behaviors of network traffic, chaotic dynamics models were proposed and helped to analyze nondeterministic network a lot. The previous research thought that the chaotic dynamics behavior was caused by random factors, and the deterministic networks would not exhibit chaotic dynamics behavior because of lacking of random factors. In this paper, we first adopted chaos theory to analyze traffic data collected from a typical deterministic network testbed — avionics full duplex switched Ethernet (AFDX, a typical deterministic network) testbed, and found that the chaotic dynamics behavior also existed in deterministic network. Then in order to explore the chaos generating mechanism, we applied the mean field theory to construct the traffic dynamics equation (TDE) for deterministic network traffic modeling without any network random factors. Through studying the derived TDE, we proposed that chaotic dynamics was one of the nature properties of network traffic, and it also could be looked as the action effect of TDE control parameters. A network simulation was performed and the results verified that the network congestion resulted in the chaotic dynamics for a deterministic network, which was identical with expectation of TDE. Our research will be helpful to analyze the traffic complicated dynamics behavior for deterministic network and contribute to network reliability designing and analysis.
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Mykyjchuk, Mykola, and Nataliya Zihanshyn. "MODELING A NETWORK OF UNMANNED AERIAL VEHICLES." Measuring Equipment and Metrology 82, no. 3 (2021): 42–48. http://dx.doi.org/10.23939/istcmtm2021.03.042.
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The research concerns the methods of UAV group control in networks with duplex communication between nodes built on the "client-server" architecture. Such systems belong to self-organized networks with variable topology. It is important to study the allowable parameters of deviation from the task in the management of a group of UAVs and analysis of the network topology for the group flighting. The network was optimized according to the Ant Colony algorithm ACO. The application of different types of algorithms prevents routing problems in networks, such as ANTMANET, AntNet, ACODV and others. Methods of coordination of group joint actions are considered. A method is proposed by which the optimal number of UAVs operated by one operator can be determined and the traffic intensity of the communication channel is calculated, as well as the bandwidth and packet transmission delay are estimated.
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Cui, Yaxin, Faez Ahmed, Zhenghui Sha, et al. "A Weighted Statistical Network Modeling Approach to Product Competition Analysis." Complexity 2022 (January 4, 2022): 1–16. http://dx.doi.org/10.1155/2022/9417869.
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Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks.
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P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling." March 2020 2, no. 1 (2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.1.006.
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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.
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P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling." March 2020 2, no. 1 (2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.3.006.
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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.
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Iram, Masood Hamdani, and Kumar Jain Raj. "Modeling and Analysis of Energy Efficient Cellular Networks." International Journal of Trend in Scientific Research and Development 3, no. 2 (2019): 373–77. https://doi.org/10.31142/ijtsrd21381.
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Energy consumption has become a primary concern in design and operation of wireless communication networks due to two main reasons Environmental concerns and Cost. The next generation network systems will have to consider energy efficient designs in any aspect. The 5G network which is most awaited today though proposes better data rates but also speaks about energy efficiency in its agenda. Iram Masood Hamdani | Raj Kumar Jain "Modeling and Analysis of Energy Efficient Cellular Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21381.pdf
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Westaby, James D. "Modeling Massive Social Network Problem Solving via Network Goal Analysis vs. Social Network Analysis." Academy of Management Proceedings 2020, no. 1 (2020): 14393. http://dx.doi.org/10.5465/ambpp.2020.14393abstract.
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van Drongelen, Wim. "Modeling Neural Activity." ISRN Biomathematics 2013 (March 7, 2013): 1–37. http://dx.doi.org/10.1155/2013/871472.
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This paper provides an overview of different types of models for studying activity of nerve cells and their networks with a special emphasis on neural oscillations. One part describes the neuronal models based on the Hodgkin and Huxley formalism first described in the 1950s. It is discussed how further simplifications of this formalism enable mathematical analysis of the process of neural excitability. The focus of the paper’s second component is on network activity. Understanding network function is one of the important frontiers remaining in neuroscience. At present, experimental techniques can only provide global recordings or samples of the activity of the huge networks that form the nervous system. Models in neuroscience can therefore play a critical role by providing a framework for integration of necessarily incomplete datasets, thereby providing insight into the mechanisms of neural function. Network models can either explicitly contain individual network nodes that model the neurons, or they can be based on representations of compound population activity. The latter approach was pioneered by Wilson and Cowan in the 1970s. Finally I provide an overview and discuss how network models are employed in the study of neuronal network pathology such as epilepsy.
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Labinsky, Alexander. "NEURAL NETWORK APPROACH TO COGNITIVE MODELING." MONITORING AND EXPERTISE IN SAFETY SYSTEM 2024, no. 3 (2024): 38–44. http://dx.doi.org/10.61260/2304-0130-2024-3-38-44.
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Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.
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PATHAK, NISHITH, SANDEEP MANE, and JAIDEEP SRIVASTAVA. "ANALYSIS OF COGNITIVE SOCIAL AND KNOWLEDGE NETWORKS FROM ELECTRONIC COMMUNICATION." International Journal of Semantic Computing 01, no. 01 (2007): 87–120. http://dx.doi.org/10.1142/s1793351x0700007x.
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This paper explains the classical social network analysis and discusses how computer networks effect a shift in constructing social networks. The paper then concentrates on analyzing cognitive aspects of a social network, explaining a simple but scalable approach for modeling a socio-cognitive network. Novel measures using such a socio-cognitive network model are defined and applications of such measures to extract useful information is illustrated on the Enron email dataset. The paper then describes a Dempster-Schafer theory based approach towards modeling a cognitive knowledge network and uses the Enron email dataset to illustrate how the proposed model can be used to capture actors' perceptions in a knowledge network. The paper concludes with a summary of the proposed models and a discussion on new research directions that can arise due to such cognitive analyses of electronic communication data.
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Pfaltz, John L. "A Set-Theoretic Approach to Modeling Network Structure." Algorithms 14, no. 5 (2021): 153. http://dx.doi.org/10.3390/a14050153.
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Three computer algorithms are presented. One reduces a network N to its interior, I. Another counts all the triangles in a network, and the last randomly generates networks similar to N given just its interior I. However, these algorithms are not the usual numeric programs that manipulate a matrix representation of the network; they are set-based. Union and meet are essential binary operators; contained_in is the basic relational comparator. The interior I is shown to have desirable formal properties and to provide an effective way of revealing “communities” in social networks. A series of networks randomly generated from I is compared with the original network, N.
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Ramli, A., N. Zulkifli, and S. M. Idrus. "Power Consumption Modeling and Analysis of Integrated Optical-Wireless Access Network." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3475. http://dx.doi.org/10.11591/ijece.v7i6.pp3475-3483.
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The integration of optical and wireless technologies at access networks are considered as a future solution which provide both high bandwidth and high mobility in an efficient way. GPON is a suitable candidate for optical backhaul due to the combination of higher data rates, greater split ratio and support for triple play services hence it offers maximum flexibility and cost advantages. On the other hand, recent developments of new radio access technologies and introduction of femtocell base stations provide the potential of offering broadband services and applications to everyone and everywhere. However, the power consumption of this network demands a particular attention because access networks are the largest contributor the network related electricity consumption. Therefore, in this paper we evaluate the power consumption of integrated optical-wireless access network which is based on independent ONU-BS architecture. We proposed a power consumption model for such network and the assessment has been done under different simulation scenarios. The constructed model will provide insight of the energy performance of the integrated access network so that in the network design process, focus can be done to the most energy saving strategies.
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A., Ramli, Zulkifli N., and M. Idrus S. "Power Consumption Modeling and Analysis of Integrated Optical-Wireless Access Network." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3475–83. https://doi.org/10.11591/ijece.v7i6.pp3475-3483.
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The integration of optical and wireless technologies at access networks are considered as a future solution which provide both high bandwidth and high mobility in an efficient way. GPON is a suitable candidate for optical backhaul due to the combination of higher data rates, greater split ratio and support for triple play services hence it offers maximum flexibility and cost advantages. On the other hand, recent developments of new radio access technologies and introduction of femtocell base stations provide the potential of offering broadband services and applications to everyone and everywhere. However, the power consumption of this network demands a particular attention because access networks are the largest contributor the network related electricity consumption. Therefore, in this paper we evaluate the power consumption of integrated optical-wireless access network which is based on independent ONU-BS architecture. We proposed a power consumption model for such network and the assessment has been done under different simulation scenarios. The constructed model will provide insight of the energy performance of the integrated access network so that in the network design process, focus can be done to the most energy saving strategies.
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McGregor, Sue L. T. "Modeling the Evolution of a Policy Network Using Network Analysis." Family and Consumer Sciences Research Journal 32, no. 4 (2004): 382–407. http://dx.doi.org/10.1177/1077727x04263840.
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Goay, Chan Hong, Azniza Abd Aziz, Nur Syazreen Ahmad, and Patrick Goh. "Progress in neural network based techniques for signal integrity analysis–a survey." Bulletin of Electrical Engineering and Informatics 8, no. 1 (2019): 276–82. http://dx.doi.org/10.11591/eei.v8i1.1405.
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With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data.
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Chan, Hong Goay, Abd Aziz Azniza, Syazreen Ahmad Nur, and Goh Patrick. "Progress in neural network based techniques for signal integrity analysis–a survey." Bulletin of Electrical Engineering and Informatics 8, no. 1 (2019): 276–82. https://doi.org/10.11591/eei.v8i1.1405.
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With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data.
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Song, Xiao, Li Dong Qian, Xue Cheng Shi, and Shao Yun Zhang. "Modeling and Analysis of C2 Network System." Applied Mechanics and Materials 148-149 (December 2011): 1554–57. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.1554.
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A command and control (C2) simulation system, ZZSIM is built. The characteristics of ZZSIM models are discussed, comparing System of Systems, HLA systems and complex systems. It is concluded that HLA based C2 simulation is valid. Then the communication data among C2 simulation nodes are recorded and analyzed, which shows that ZZSIM is scale-free. Therefore the data verify that ZZSIM is a valid simulation system of network centric war.
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Salah, K., K. Elbadawi, and R. Boutaba. "Performance Modeling and Analysis of Network Firewalls." IEEE Transactions on Network and Service Management 9, no. 1 (2012): 12–21. http://dx.doi.org/10.1109/tnsm.2011.122011.110151.
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Cao, Kehao. "Research and Analysis on Network Security Modeling." International Journal of Security and Its Applications 10, no. 4 (2016): 135–42. http://dx.doi.org/10.14257/ijsia.2016.10.4.14.
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Qiao, Hu, Zhaohui Xu, Jiang He, and Ying Xiang. "Product Module Network Modeling and Evolution Analysis." Computational Intelligence and Neuroscience 2019 (March 6, 2019): 1–8. http://dx.doi.org/10.1155/2019/2186916.
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Modular technology for product design and manufacturing is an effective way to solve mass customization problems. One difficulty in the application of modular technology is that the characteristics of mass customization, such as multi batch and small batch, easily increase the complexity of the module structure of the enterprise products. To address this problem, based on complex network theory, the enterprise products module is mapped as the vertex of the network, the number of modules used is mapped as the node weight, the dependency between the modules is mapped to the edge, and the product module network is established. The brittleness risk entropy of the product module network is put forward by considering the internal and external factors that influence the application of the enterprise module to determine the rationality of the required modules' organizational structures. Then, the stability uncertainty of the product module network can be determined by calculating the brittleness risk entropy, in which the subsystem that is the most brittle risk entropy can be identified. And the evolution of the product module network can be promoted by changing factors of the entropy maximum subsystem. To analyze the change in the product module network caused by module evolution, a BBV (Barrat–Barthelemy–Vespignani) model of the product module network is established to dynamically determine the brittle risk of the product module network. Finally, the modularity structure of a series of special vehicles is used as an example to verify the presented method, and the results confirm the rationality and effectiveness of the method.
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B. Govan, Paul. "BayesianNetwork: Interactive Bayesian Network Modeling and Analysis." Journal of Open Source Software 3, no. 21 (2018): 425. http://dx.doi.org/10.21105/joss.00425.
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Li, Shuping, and Zhen Jin. "Modeling and Analysis of New Products Diffusion on Heterogeneous Networks." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/940623.
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We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.
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Takaffoli, Mansoureh, Farzad Sangi, Justin Fagnan, and Osmar Zaiane. "MODEC — Modeling and Detecting Evolutions of Communities." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 626–29. http://dx.doi.org/10.1609/icwsm.v5i1.14182.
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Social network analysis encompasses the study of networked data and examines questions related to structures and patterns that can lead to the understanding of the data and the intrinsic relationships, such as identifying influential nodes, recognizing critical paths, predicting unobserved relationships, discovering communities, etc. All of these analyses, germane to a variety of application domains, are typically done on static information networks; that is, a fixed snapshot of the information network. Yet, a social network changes and understanding the evolution of the network and detecting these changes in the underlying structures is paramount for a multitude of applications. Looking at networks as fixed snapshots misses the opportunity to capture the evolutionary patterns. In this paper, we present a framework for modeling community evolution in social networks by tracking of events related to the life cycle of a community. We illustrate the capabilities of our framework by applying it to real datasets and validate the results using topics extracted from the tracked communities.
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Park, Sangsung, and Sunghae Jun. "Patent Analysis Using Bayesian Data Analysis and Network Modeling." Applied Sciences 12, no. 3 (2022): 1423. http://dx.doi.org/10.3390/app12031423.
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Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology.
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Qi, Yan Bo, Liu Zhong, Le Zhang, and Jiang Hu Xu. "Joint Combat System of Systems Modeling and Effectiveness Analysis Based on Complex Networks." Advanced Materials Research 591-593 (November 2012): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1589.
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How to describe the difference among various combat networks and measure their effectiveness is an important problem in combat analysis. In this paper three basic network models are developed based on the theory of complex networks and a new method is put forward for measuring the network effect of combat SoS. Numerical comparisons of the three combat network models indicate that though integrated joint operations network has the highest networked effects in networked effectiveness and clustering coefficient, but when considering the Average Path Length index, it has the lowest effectiveness. The results also suggest that the degree distribution of integrated joint operations network is scale-free thus it has the highest survivability.
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He, Song, Can Zhang, Wei Guo, and Li-Dong Zhai. "Worms Propagation Modeling and Analysis in Big Data Environment." International Journal of Distributed Sensor Networks 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/985856.
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The integration of the Internet and Mobile networks results in huge amount of data, as well as security threat. With the fragile capacity of security protection, worms can propagate in the integration network and undermine the stability and integrity of data. The propagation of worm is a great security risk to massive amounts of data in the integration network. We propose a kind of worm propagating in big data environment named BD-Worm. BD-Worm consumes computing resources and gets privacy information of users, which causes huge losses to our working and living. This paper constructs an integration network topology model and designs the BD-Worm propagating in the big data environment. To analyze the propagation of BD-Worm, we conduct a simulation and provide some recommendations to contain the widespread of BD-Worm according to the simulation results.
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Kovernikova, L., and V. C. Luong. "Nonlinear load modeling for analysis of non-sinusoidal conditions in electrical networks based on measurements of harmonic parameters." Energy Systems Research, no. 3(15) (November 30, 2021): 5–20. http://dx.doi.org/10.38028/esr.2021.03.0001.
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Non-sinusoidal conditions in electrical networks need to be calculated for their control and development of technical measures to maintain harmonic parameters according to the requirements of regulatory documents. These calculations are impossible without electrical network and nonlinear load models that adequately reflect them in computational programs. Nonlinear load models have been developed for a long time. Some studies present general modeling principles and models of various nonlinear devices. Others consider some nonlinear devices as equivalent nonlinear loads connected to low and medium voltage networks. A whole host of high-power nonlinear electrical equipment is connected to high voltage networks. Modeling nonlinear loads connected to these networks is a problem. Research of measured parameters of harmonic conditions in electrical networks has shown that they are random values. The probabilistic nature is determined by the network configuration, a range of network components, the number of nonlinear loads, wave and frequency properties of the network, harmonic source phase currents, voltage at terminals of nonlinear electrical equipment, changes in operating conditions and load power, and many other factors. Nonlinear loads can only be modeled based on the measurements of parameters of harmonic conditions due to their unpredictability. The paper presents an overview of existing methods for modeling nonlinear loads, a methodological approach to modeling nonlinear loads based on measured parameters, an algorithm for modeling harmonics of active and reactive currents, a computational program algorithm designed to identify distribution functions of measured current harmonics, and modeling results for current harmonics of railway transformers supplying power to electric locomotives.
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S., Manikandan, and Manikanda Kumaran K. "Performance Analysis of Mobile Ad-Hoc Network Routing Protocols using Network Simulator – 2." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 06 (2014): 957–60. https://doi.org/10.5281/zenodo.14742847.
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Ad-hoc network is a network which consists of nodes that use a wireless interface to send packet data. Since the nodes in a network of this kind can serve as routers and hosts, they can forward packets on behalf of other nodes and run user application. A mobile ad-hoc network (MANET) is probably the most well-known example of this networking paradigm have been around for over twenty years, mainly exploited to design tactical networks. Furthermore, the multi-hop ad-hoc networking paradigm is often used for building sensor networks to study, control, monitor events and phenomena. To exploit these potentialities, modeling, simulation and theoretical analyses have to be complemented by real experiences, which provide both a direct evaluation of ad-hoc networks and at the same time precious information for a realistic modeling of these systems. Different routing protocols namely Ad-hoc On-demand Distance Vector (AODV) protocol, Dynamic Source Routing (DSR) protocol and Destination Sequenced Distance Vector (DSDV) protocol in MANET are compared and the performance are evaluated based on various metrics like Packet Delivery ratio, Avg. endto-end delay, throughput, etc. For this purpose, a discrete event simulator known as NS2 is used.
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Wiedemann, Nina, Henry Martin, and Martin Raubal. "Unlocking social network analysis methods for studying human mobility." AGILE: GIScience Series 3 (June 10, 2022): 1–12. http://dx.doi.org/10.5194/agile-giss-3-19-2022.
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Abstract. Planning and operations in urban spaces are strongly affected by human mobility behavior. A better understanding of individual mobility is key to improve transportation systems and to guide the allocation of public space. Previous studies have discovered statistical laws of travel distances, but the topology of movement between places has received little attention. We propose to employ network modelling methods to analyze the effect of spatial and context attributes on individual movement patterns. The perspective of mobility as a network allows to explicitly regard dyadic dependencies of sequential location visits. Here, we consider two methods developed for social networks and provide a formulation of mobility networks to justify their applicability. First, we use the Multiple Regression Quadratic Assignment Procedure to test hypotheses on the influence of location attributes on mobility behavior. Secondly, Stochastic Actor-Oriented Models are applied to model the evolution of mobility networks over time. As a proof-of-concept study, we transform data from one GNSS-based and one check-in based dataset into mobility networks and present results from both methods. We find relations that appear for a majority of samples and thus seem inherent to mobility networks. The differences between individuals and the available datasets are further quantified and discussed. We conclude that the transfer of network modeling methods is an interesting opportunity to study network-related phenomena in geographic information science.
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Han, Haiyan, and Rennong Yang. "Improvement on Load-Induced Cascading Failure in Asymmetrical Interdependent Networks: Modeling and Analysis." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/194568.
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Many real-world systems can be depicted as interdependent networks and they usually show an obvious property of asymmetry. Furthermore, node or edge failure can trigger load redistribution which leads to a cascade of failure in the whole network. In order to deeply investigate the load-induced cascading failure, firstly, an asymmetrical model of interdependent network consisting of a hierarchical weighted network and a WS small-world network is constructed. Secondly, an improved “load-capacity” model is applied for node failure and edge failure, respectively, followed by a series of simulations of cascading failure over networks in both interdependent and isolated statuses. The simulation results prove that the robustness in isolated network changes more promptly than that in the interdependent one. Network robustness is positively related to “capacity,” but negatively related to “load.” The hierarchical weight structure in the subnetwork leads to a “plateau” phenomenon in the progress of cascading failure.
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Kabzeva, Aneta, Joachim Götze, and Paul Müller. "Model-Based Relationship Management for Service Networks." International Journal of Systems and Service-Oriented Engineering 5, no. 4 (2015): 104–32. http://dx.doi.org/10.4018/ijssoe.2015100105.
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With the broad adoption of service-orientation for the realization of business applications and their provisioning and usage over cloud infrastructures, the topology of the resulting service networks is becoming extremely complex. Due to the composition of services for value-added business capabilities and the reusability of a service in multiple compositions, the execution of one service often depends on other services and changes in its provisioning can affect the health of large parts of the service network. The lack of insight on the relationships between the network components makes the management of the service network's health hard and error prone tasks. This article introduces a service network modeling approach for capturing the topology of a service network at design time. The approach considers the complete modeling process from representation, through collection, to analysis of the relationship information. The major contributions are a generic and adaptable modeling structure, a classification of service network entities and relationships, and a modular management framework automating the modeling process.
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Zhang, Yang, Edwin K. P. Chong, Jan Hannig, and Donald Estep. "Continuum Modeling and Control of Large Nonuniform Wireless Networks via Nonlinear Partial Differential Equations." Abstract and Applied Analysis 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/262581.
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We introduce a continuum modeling method to approximate a class of large wireless networks by nonlinear partial differential equations (PDEs). This method is based on the convergence of a sequence of underlying Markov chains of the network indexed byN, the number of nodes in the network. AsNgoes to infinity, the sequence converges to a continuum limit, which is the solution of a certain nonlinear PDE. We first describe PDE models for networks with uniformly located nodes and then generalize to networks with nonuniformly located, and possibly mobile, nodes. Based on the PDE models, we develop a method to control the transmissions in nonuniform networks so that the continuum limit is invariant under perturbations in node locations. This enables the networks to maintain stable global characteristics in the presence of varying node locations.
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Luo, Guilan, Anshun Hu, Caikui Wang, Xin Ma, Lianbiao Fang, and Xuan Liu. "Multi-factor network modeling and simulation analysis of time-series dynamic and static climate." Journal of Physics: Conference Series 2504, no. 1 (2023): 012050. http://dx.doi.org/10.1088/1742-6596/2504/1/012050.
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Abstract With the development of complex networks and big data disciplines, complex networks and big data are widely used in various fields to reveal the changing characteristics of things. This paper applies the method of complex network to climatology, explores the complex interaction of various climatic factors in China’s regional climate system with the method of complex network, selects the meteorological big data of 288 meteorological stations in China from 1984 to 2016, defines the meteorological stations and connecting network under various climatic factors as a single factor climate network, and adds the connecting edge between meteorological stations with different climatic factors on this basis, Then the multi-factor climate network is constructed by the network characteristic parameters. The analysis of correlation and stability shows that the correlation of climate network from strong to weak is spring, autumn, winter and summer; In the process of 33 years of time series change, the correlation of climate network shows a weak downward trend, with strong stability and good viability.
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Zhao, Yaning, and Chengyi Xia. "Crash behavior modeling and analysis on two interdependent networks." Modern Physics Letters B 35, no. 11 (2021): 2150182. http://dx.doi.org/10.1142/s0217984921501827.
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In real-world systems, one random failure or targeted attack may lead to the cascading destruction within a system, and even create the systemic collapse among multiple correlated ones. With the help of network science, modeling the cascade failure on complex networks has become a challenging topic. In this paper, we put forward an improved cascading model on two interdependent networks to further explore the impact of the specific detachment logic on the system collapse, where the distinct condition to leave the system for a pair of nodes exists on the two-layered networks. Meanwhile, once the detachment logic is satisfied, two different criteria are adopted to determine whether this pair of nodes will leave the interdependent networks. Through extensive numerical simulations, we analyze the effects of detachment logic, network topology, departure criteria and nodal coupling relationship between layers in detail. It is found that in our detachment logic, both criteria will render the whole system to exhibit the phenomenon of pseudo-steady state and sudden collapse. In particular, two critical thresholds to characterize the evolution of system crashing emerge, which is different from previous findings under other detachment logic conditions on two-layered networks. Current results are conducive to further understanding the crashing behaviors of interdependent networks and designing the more robust networked systems in practice.
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Chen, Xi, David Banks, and Mike West. "Bayesian dynamic modeling and monitoring of network flows." Network Science 7, no. 3 (2019): 292–318. http://dx.doi.org/10.1017/nws.2019.10.
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AbstractIn the context of a motivating study of dynamic network flow data on a large-scale e-commerce website, we develop Bayesian models for online/sequential analysis for monitoring and adapting to changes reflected in node–node traffic. For large-scale networks, we customize core Bayesian time series analysis methods using dynamic generalized linear models (DGLMs). These are integrated into the context of multivariate networks using the concept of decouple/recouple that was recently introduced in multivariate time series. This method enables flexible dynamic modeling of flows on large-scale networks and exploitation of partial parallelization of analysis while maintaining coherence with an over-arching multivariate dynamic flow model. This approach is anchored in a case study on Internet data, with flows of visitors to a commercial news website defining a long time series of node–node counts on over 56,000 node pairs. Central questions include characterizing inherent stochasticity in traffic patterns, understanding node–node interactions, adapting to dynamic changes in flows and allowing for sensitive monitoring to flag anomalies. The methodology of dynamic network DGLMs applies to many dynamic network flow studies.
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MNSSVKR Gupta, V., and Ch V.Phani Krishna. "K-core analysis and modeling for network centralities." International Journal of Engineering & Technology 7, no. 2.7 (2018): 168. http://dx.doi.org/10.14419/ijet.v7i2.7.10285.
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Network modeling is the interdisciplinary study of relationships. Network analysis deals with relational data and Network modeling represents the interdisciplinary study of relationships. Network structure can be studied at many different levels. Around 1000 article titles on cancer, published in journal resources were considered as a dataset. Data exploration was done through displaying nodes and edges in various layouts. With a term frequency limit of 100, nearly 64 terms appeared which less than 1% sparse is. Word cloud data was plotted using word frequencies from term matrix data. An undirected network graph plotted and evaluated density, average path length and modularity, which were found to be within limits. K-cores have also been used to analyze the connectivity of a network. Network centralities such as Degree centrality, Closeness, Eigenvector and between’s centrality resulted in node carcinoma being more central in the network.
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Manzoni, Pietro, Vittorio Maniezzo, and Marco A. Boschetti. "Modeling Distributed MQTT Systems Using Multicommodity Flow Analysis." Electronics 11, no. 9 (2022): 1498. http://dx.doi.org/10.3390/electronics11091498.
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The development of technologies that exploit the Internet of Things (IoT) paradigm has led to the increasingly widespread use of networks formed by different devices scattered throughout the territory. The Publish/Subscribe paradigm is one of the most used communication paradigms for applications of this type. However, adopting these systems due to their centralized structure also leads to the emergence of various problems and limitations. For example, the broker is typically the single point of failure of the system: no communication is possible if the broker is unavailable. Moreover, they may not scale well considering the massive numbers of IoT devices forecasted in the future. Finally, a network architecture with a single central broker is partially at odds with the edge-oriented approach. This work focuses on the development of an adaptive topology control approach, able to find the most efficient network configuration maximizing the number of connections and reduce the waste of resources within it, starting from the definition of the devices and the connections between them present in the system. To reach the goal, we leverage an integer linear programming mathematical formulation, providing the basis to solve and optimize the problem of network configuration in contexts where the resources available to the devices are limited.
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Elwert, Frederik, and Sven Sellmer. "Modeling Structure and Content: Socio-Semantic Network Analysis of the Mahābhārata." Leonardo 50, no. 5 (2017): 501. http://dx.doi.org/10.1162/leon_a_01277.
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There is a demand to incorporate content information into social networks. The authors constructed and visualized a network of the most important gods and heroes in the Sanskrit epic Mahābhārata. The network includes semantic information about the actors and their relationships. These two types of information were collected automatically with the help of the Nubbi topic modeling algorithm, which assigns separate sets of topics to both persons and their relations. The visualization of such a network provides intuitive access to a high density of information, like the topic distribution for each actor and the predominant topic for each relation.
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Hamdani, Iram Masood, and Raj Kumar Jain. "Modeling and Analysis of Energy Efficient Cellular Networks." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (2019): 373–77. http://dx.doi.org/10.31142/ijtsrd21381.
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Wei, Guanghui. "System Reliability Modeling and Analysis of Distributed Networks." Advances in Multimedia 2022 (January 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/9719427.
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The reliability of distributed network is researched by establishing one set of effective modeling, analysis, evaluation, and optimization method theory, which is a hot research area in the distributed network system field. The phased analysis method is utilized to study the reliability of the distributed network including the analysis and definition of the distributed network on the basis of studying its operational process. Meanwhile, task request, task scheduling, and execution phase will be also studied, and the establishment of reliability mathematical model of distributed network will be discussed to provide actual reference significance for the design of the distributed network system.
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Paszkiewicz, Andrzej. "Modeling and Analysis of Anomalies in the Network Infrastructure Based on the Potts Model." Entropy 23, no. 8 (2021): 949. http://dx.doi.org/10.3390/e23080949.
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The paper discusses issues concerning the occurrence of anomalies affecting the process of phase transitions. The considered issue was examined from the perspective of phase transitions in network structures, particularly in IT networks, Internet of Things and Internet of Everything. The basis for the research was the Potts model in the context of IT networks. The author proposed the classification of anomalies in relation to the states of particular nodes in the network structure. Considered anomalies included homogeneous, heterogeneous, individual and cyclic disorders. The results of tests and simulations clearly showed the impact of anomalies on the phase transitions in the network structures. The obtained results can be applied in modelling the processes occurring in network structures, particularly in IT networks.
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Liang, Li, Feikai Qi, and Shicai Gong. "NETWORK ANALYSIS OF ETHEREUM LAYER 2." Acta Electronica Malaysia 8, no. 1 (2024): 27–30. https://doi.org/10.26480/aem.01.2024.27.30.
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The cryptocurrency market has experienced remarkable growth and garnered widespread attention in recent years. Ethereum, as one of the most well-known cryptocurrencies, has faced criticism for its network congestion and high transaction fees. To address this issue, developers have proposed various scalability solutions. In this paper, we compare two different network solutions, Polygon and Arbitrum, to the Ethereum. Based on the research methodology of complex networks, this study encompasses various stages ranging from data collection and processing to the modeling and analysis of transaction networks. Our findings reveal that Arbitrum shows an advantage over Polygon when comparing the networks at the same transaction volume. With sufficient development, Rollups has the potential to become the preferred scaling solution for Ethereum. Additionally, our analysis illustrates that all transaction networks exhibit a small-world phenomenon when they reach a significant size.
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Yang, Yixin, and Jin Chen. "Analysis of Interprovincial Trade Relationships Based on Improved Gravity Modeling." Frontiers in Humanities and Social Sciences 4, no. 4 (2024): 1–6. http://dx.doi.org/10.54691/6mmwjv38.
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This paper analyzes the inter-provincial trade flows estimated by the improved gravity model using the complex network method. The article collects the distance and annual GDP value of each province from Baidu map and China Statistical Yearbook, and uses the gravity model to estimate the inter-provincial trade flow. Considering that the inter-provincial trade in the current year may be related to the GDP value of the previous year, the original model is improved by introducing the Gi-1 index and the results are normalized by multiplying them by 100 as the weights of the network constructed, and a fully connected network is established with the name of each province as a node connected network, and do the analysis of the network's node strength, weighted tight centrality, weight entropy of the three indicators, while using spectral clustering to further explore the trade relationship between the provinces.
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Bidaibekov, Ye Y., Sh T. Shekerbekova, and Ye H. Zhabayev. "EXPERIMENTAL VERIFICATION OF THE EFFECTIVENESS OF COMPUTER NETWORK TRAINING BASED ON NETWORK MODELING." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (2021): 143–48. http://dx.doi.org/10.51889/2021-4.1728-7901.19.
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The article discusses the effectiveness of teaching computer networks to future computer science teachers based on network modeling. The experiment was conducted on the basis of the Institute of Mathematics, Physics and Informatics of the Abai Kazakh National Pedagogical University and the Kazakh National Women's Pedagogical University. Methods and techniques for organizing and conducting practical work on teaching computer networking to future teachers of computer science on the basis of network modeling are described. Analysis of the results of experimental work is presented graphically in the form of tables, diagrams. The pedagogical experiment was conducted in three stages: defining, formative, final. The following research methods were used during the experimental work: analysis, generalization, systematization, observation, questionnaire, testing, research and generalization of pedagogical experience. As a result of experimental work, the effectiveness of the developed methodology for training in computer networks based on network modeling is tested.
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Bidaibekov, Ye Y., Sh T. Shekerbekova, and Ye H. Zhabayev. "EXPERIMENTAL VERIFICATION OF THE EFFECTIVENESS OF COMPUTER NETWORK TRAINING BASED ON NETWORK MODELING." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (2021): 143–48. http://dx.doi.org/10.51889/2021-4.1728-7901.19.
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The article discusses the effectiveness of teaching computer networks to future computer science teachers based on network modeling. The experiment was conducted on the basis of the Institute of Mathematics, Physics and Informatics of the Abai Kazakh National Pedagogical University and the Kazakh National Women's Pedagogical University. Methods and techniques for organizing and conducting practical work on teaching computer networking to future teachers of computer science on the basis of network modeling are described. Analysis of the results of experimental work is presented graphically in the form of tables, diagrams. The pedagogical experiment was conducted in three stages: defining, formative, final. The following research methods were used during the experimental work: analysis, generalization, systematization, observation, questionnaire, testing, research and generalization of pedagogical experience. As a result of experimental work, the effectiveness of the developed methodology for training in computer networks based on network modeling is tested.