Journal articles: 'Network modelling'

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Maciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez, and Jose Manuel Sánchez-Bernabeu. "Conceptual Modelling of Complex Network Management Systems." Journal of Computers 10, no. 5 (2015): 309–20. http://dx.doi.org/10.17706/jcp.10.5.309-320.

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Smirnov, Alexander, and Nikolay Shilov. "Business Network Modelling." International Journal of Information System Modeling and Design 1, no. 4 (October 2010): 77–91. http://dx.doi.org/10.4018/jismd.2010100104.

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Business networks have appeared as a reaction to changes taking place in the world economy and logistic networks can be considered as examples of such networks. The approach proposed in the paper is based on the idea to represent the business network members with services provided by them, and to achieve interoperability via application of the SOA standards. The approach is based on usage of such technologies as Web services, ontology, and context management. Web services enable interoperability at the technological level. Ontologies are used for description of knowledge domains and enable interoperability at the level of semantics. The purpose of the context is to represent only relevant information from the large amount of the information and the application of the approach is demonstrated on the case study from the area of dynamic logistics. The considered problem takes into account a continuously changing problem environment and requires nearly real-time solving.

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Pugacheva, E. "Social Network Modelling." Мiждисциплiнарнi дослiдження складних систем, no. 6 (2015): 49–62. http://dx.doi.org/10.31392/2307-4515/2015-6.2.

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Clark, J. W. "Neural network modelling." Physics in Medicine and Biology 36, no. 10 (October 1, 1991): 1259–317. http://dx.doi.org/10.1088/0031-9155/36/10/001.

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Lambert, Jean, and Béatrice Stéphan. "Modelling network profiles." Computer Communications 18, no. 9 (September 1995): 600–608. http://dx.doi.org/10.1016/0140-3664(95)99803-k.

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Tumbas, Pere, Predrag Matkovic, and Marton Sakal. "Modelling cross-border regional network for innovative development." Perspectives of Innovations, Economics and Business 13, no. 3 (July 16, 2013): 16–24. http://dx.doi.org/10.15208/pieb.2013.13.

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

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

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Cebolledo Gutierrez, Edgar Omar, and Olga De Troyer. "Modelling Social Network Interactions in Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 4 (June 24, 2021): 82–88. http://dx.doi.org/10.1609/aiide.v11i4.12831.

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Social Networks, like Facebook, have become an important part of life for millions of people, with many people using them on a daily basis. While there are games that simulate life-to-life interactions, there are not as many that simulate social network interactions, and even fewer where social network interactions are an important part of the gameplay. Having models for the simulation of interactions on social networks is the first step needed to be able to create games where a social network is an important component of the gameplay. In this paper we present a model to create dy-namic and believable interactions in social networks and in-dicate how it was used in a serious game about cyberbullying. The model is based on personality models and Berne’s so-called Social Games.

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Mondragón, Raúl J. "Topological modelling of large networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1872 (March 6, 2008): 1931–40. http://dx.doi.org/10.1098/rsta.2008.0008.

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In a complex network, there is a strong interaction between the network's topology and its functionality. A good topological network model is a practical tool as it can be used to test ‘what-if’ scenarios and it can provide predictions of the network's evolution. Modelling the topology structure of a large network is a challenging task, since there is no agreement in the research community on which properties of the network a model should be based, or how to test its accuracy. Here we present recent results on how to model a large network, the autonomous system (AS)-Internet, using a growth model. Based on a nonlinear preferential growth model and the reproduction of the network's rich club, the model reproduces many of the topological characteristics of the AS-Internet. We also identify a recent method to visualize the network's topology. This visualization technique is simple and fast and can be used to understand the properties of a large complex network or as a first step to validate a network model.

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Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

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Ghaboussi, J., X. Wu, and G. Kaklauskas. "NEURAL NETWORK MATERIAL MODELLING." Statyba 5, no. 4 (January 1999): 250–57. http://dx.doi.org/10.1080/13921525.1999.10531472.

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Novokhrestov, A., A. Konev, A. Shelupanov, and A. Buymov. "Computer network threat modelling." Journal of Physics: Conference Series 1488 (March 2020): 012002. http://dx.doi.org/10.1088/1742-6596/1488/1/012002.

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Burnell, David, and Julia Race. "Network Asset Performance Modelling." OR Insight 15, no. 1 (January 2002): 3–14. http://dx.doi.org/10.1057/ori.2002.2.

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Hou, Jennifer C., and P. R. Kumar. "Network modelling and simulation." Computer Networks 50, no. 12 (August 2006): 1885–86. http://dx.doi.org/10.1016/j.comnet.2005.10.025.

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Cudmore, Peter, and Edmund J. Crampin. "Bondgraphtools: Modelling Network Bioenergetics." Biophysical Journal 116, no. 3 (February 2019): 420a. http://dx.doi.org/10.1016/j.bpj.2018.11.2262.

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Harby, Atle. "European aquatic modelling network." River Research and Applications 23, no. 5 (2007): 467–68. http://dx.doi.org/10.1002/rra.996.

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Prinz, Astrid A. "Computational approaches to neuronal network analysis." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1551 (August 12, 2010): 2397–405. http://dx.doi.org/10.1098/rstb.2010.0029.

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Computational modelling is an approach to neuronal network analysis that can complement experimental approaches. Construction of useful neuron and network models is often complicated by a variety of factors and unknowns, most notably the considerable variability of cellular and synaptic properties and electrical activity characteristics found even in relatively ‘simple’ networks of identifiable neurons. This chapter discusses the consequences of biological variability for network modelling and analysis, describes a way to embrace variability through ensemble modelling and summarizes recent findings obtained experimentally and through ensemble modelling.

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Anzellotti, Stefano, Dorit Kliemann, Nir Jacoby, and Rebecca Saxe. "Directed network discovery with dynamic network modelling." Neuropsychologia 99 (May 2017): 1–11. http://dx.doi.org/10.1016/j.neuropsychologia.2017.02.006.

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Ma, Hongyang, Qile Zhao, Sandra Verhagen, Dimitrios Psychas, and Han Dun. "Kriging Interpolation in Modelling Tropospheric Wet Delay." Atmosphere 11, no. 10 (October 20, 2020): 1125. http://dx.doi.org/10.3390/atmos11101125.

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This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are divided into three blocks: 5 stations located approximately in the center of the networks, 5 stations on the edge of the networks and 5 stations outside the networks. The zenith tropospheric wet delays are estimated at the network and user stations through the ionosphere-free positioning model; meanwhile, the predicted wet delays at the user stations are generated by the Kriging interpolation in the use of the tropospheric estimations at the network. The root mean square errors (RMSE) are calculated by comparing the predicted wet delays and estimated wet delays at the same user station. The results show that RMSEs of the stations inside the network are at a sub-centimeter level with an average value of 0.74 cm in the sparse network and 0.69 cm in the dense network. The stations on edge and outside the network can also achieve 1-cm level accuracy, which overcomes the limitation that accurate interpolations can only be attained inside the network. This contribution also presents an insignificant improvement of the prediction accuracy from the sparse network to the dense network over 1-year’s data processing and a seasonal effect on the tropospheric wet delay predictions.

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Durugbo, Christopher. "Bunch: a channel-oriented business network modelling approach." Business Process Management Journal 20, no. 3 (May 27, 2014): 455–79. http://dx.doi.org/10.1108/bpmj-02-2013-0017.

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Purpose – The purpose of this paper is to evaluate the benefits of using the business network channel (Bunch) approach for modelling business networks and studying the business network evolution. Business network models put the structures of process models into context by visualising roles and communication channels for social interactions. Design/methodology/approach – The research applies a case study-based approach involving the creation of business network visualisations to capture snapshots of an industrial firm's business network over a three-year period. A questionnaire-based study was also conducted with 18 key informants to evaluate the Bunch approach against existing business network modelling techniques. Findings – This study shows that when business networks – as opposed to business processes – are diagrammatically modelled, patterns of relations between individuals can also be visualised and factored into how information systems are (re)designed and deployed. The study also finds that as business networks evolve, the ability to offer complementary channels of communication and coordinate business/technological information is vital to how upturns in process times improves overall business effectiveness and efficiency. Originality/value – The major contribution of this paper is an exposition on how the Bunch approach could serve as a pedagogical tool for gaining clarity on their roles and links within the business and as an analytical tool for studying the evolution of business networks in relation to roles, links, information technologies, business strategies and business network anomalies.

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O., Amusan, Thompson A. F., Aderinola T. B., and Alese B. K. "Modelling Malicious Attack in Social Networks." Network and Communication Technologies 5, no. 1 (February 6, 2020): 37. http://dx.doi.org/10.5539/nct.v5n1p37.

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Online Social Networks (OSNs) are based on actual trust relationships in environments which help people communicate with friends, family and acquaintances. Malicious individuals take advantage of this trust relationship to propagate malware through social networks. We study the dynamics of malware propagation among OSN users. Social networks users are referred to as nodes which is in two compartments: Healthy (H), or Infected (I). A H node could either be susceptible to infection (S) or removed (R). Simulations were carried out in R using the EpiModel network simulation package. Two networks were simulated thrice with different parameters to give better average values. Two categories of nodes, first category comprises of 3000 nodes with fewer connections and the second category comprising of 7000 nodes are the influential nodes with more connections. The larger network tends to have a higher fraction of nodes getting infected per unit time due to the high level of connectivity, as opposed to the small network where the number of connections is few. However, the infection tends to persist in the network as long as the birth rate is not equal to zero.

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Fischer, M. M. "Computational Neural Networks: A New Paradigm for Spatial Analysis." Environment and Planning A: Economy and Space 30, no. 10 (October 1998): 1873–91. http://dx.doi.org/10.1068/a301873.

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In this paper a systematic introduction to computational neural network models is given in order to help spatial analysts learn about this exciting new field. The power of computational neural networks viz-à-viz conventional modelling is illustrated for an application field with noisy data of limited record length: spatial interaction modelling of telecommunication data in Austria. The computational appeal of neural networks for solving some fundamental spatial analysis problems is summarized and a definition of computational neural network models in mathematical terms is given. Three definitional components of a computational neural network—properties of the processing elements, network topology and learning—are discussed and a taxonomy of computational neural networks is presented, breaking neural networks down according to the topology and type of interconnections and the learning paradigm adopted. The attractiveness of computational neural network models compared with the conventional modelling approach of the gravity type for spatial interaction modelling is illustrated before some conclusions and an outlook are given.

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Vasiliauskas, Aidas Vasilis. "MODELLING OF INTERMODAL FREIGHT TRANSPORTATION NETWORK." TRANSPORT 17, no. 3 (June 30, 2002): 117–21. http://dx.doi.org/10.3846/16483840.2002.10414024.

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It was realized long ago that one of the main factors for steady groowing economic of the country is a properly working transport system in which new enough means of cargo transportation, so called intermodal transportation takes not the last place. First of all it raises problems related with the development and optimization of proper infrastructure - the network of roads and terminals which aim to carry out intermodal transportation. The article deals with one of possible ways of modelling the national intermodal freight transportation network. The basis for such model is the investigation of freight traffic during which we identifY the routes for intermodal freight transportation (as well as places where such freight is shifted from one mode of transport to another), extend the existing statistic date base on modal transport carriages and start to develop a new data base on intermodal freight carriages. With the help of the last one we can go further in modelling national intermodal freight transportation network and determine the steps for creating such network. The main steps should include: - the integration of the particular modal networks into a general intermodal network; - the optimization of the network defining optimal routes between possible origin and destination points; - the estimation of terminal operations to get a more detailed and optimal view of the national intermodal cargo transportation network.

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Kaveh, M., and R. A. Chayjan. "Mathematical and neural network modelling of terebinth fruit under fluidized bed drying." Research in Agricultural Engineering 61, No. 2 (June 2, 2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.

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The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40–80°C and 0.81–4.35 m/s, respectively. The best outcome for the use of ANN for the effective moisture diffusivity appertained to CFNN network with BR training algorithm, topology of 2-3-1 and threshold function of TANSIG. Similarly, the best outcome for the use of ANN for drying rate and moisture ratio also appertained to CFNN network with LM training algorithm, topology of 3-2-4-2 and threshold function of TANSIG.

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Massimo, Di Gangf. "Modelling dynamic network loading ontransportation networks through a continuous." Mathematical Modelling of Systems 2, no. 3 (January 1996): 175–96. http://dx.doi.org/10.1080/13873959608837037.

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Haq, Hafiz, Petri Välisuo, and Seppo Niemi. "Modelling Sustainable Industrial Symbiosis." Energies 14, no. 4 (February 22, 2021): 1172. http://dx.doi.org/10.3390/en14041172.

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Industrial symbiosis networks conventionally provide economic and environmental benefits to participating industries. However, most studies have failed to quantify waste management solutions and identify network connections in addition to methodological variation of assessments. This study provides a comprehensive model to conduct sustainable study of industrial symbiosis, which includes identification of network connections, life cycle assessment of materials, economic assessment, and environmental performance using standard guidelines from the literature. Additionally, a case study of industrial symbiosis network from Sodankylä region of Finland is implemented. Results projected an estimated life cycle cost of €115.20 million. The symbiotic environment would save €6.42 million in waste management cost to the business participants in addition to the projected environmental impact of 0.95 million tonne of CO2, 339.80 tonne of CH4, and 18.20 tonne of N2O. The potential of further cost saving with presented optimal assessment in the current architecture is forecast at €0.63 million every year.

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Gleiser, Pablo M., and Victor I. Spoormaker. "Modelling hierarchical structure in functional brain networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1933 (December 28, 2010): 5633–44. http://dx.doi.org/10.1098/rsta.2010.0279.

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In this work, we focus on a complex-network approach for the study of the brain. In particular, we consider functional brain networks, where the vertices represent different anatomical regions and the links their functional connectivity. First, we build these networks using data obtained with functional magnetic resonance imaging. Then, we analyse the main characteristics of these complex networks, including degree distribution, the presence of modules and hierarchical structure. Finally, we present a network model with dynamical nodes and adaptive links. We show that the model allows for the emergence of complex networks with characteristics similar to those observed in functional brain networks.

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Mukhtar, F., Y. Kuznetsov, and P. Russer. "Network modelling with Brune's synthesis." Advances in Radio Science 9 (July 29, 2011): 91–94. http://dx.doi.org/10.5194/ars-9-91-2011.

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Abstract. Network modelling of general, lossy or lossless, one-port and symmetric two-port passive electromagnetic structures in systematic manner is presented. Rational function representation of the numerical data of Z- or Y-parameters is obtained with the use of Vector Fitting procedure. A systematic strategy for obtaining equivalent lumped element circuit from the rational function, applying Brune's circuit synthesis, is also presented.

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Manvelidze, A. B. "Air lines network modelling algorithm." Strategic decisions and risk management, no. 6 (February 13, 2018): 22–29. http://dx.doi.org/10.17747/2078-8886-2017-6-22-29.

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This analysis is dedicated to find out methods for setting of route networks where new aircraft can be effectively put into service. The conception of this analysis is based on the idea of so called connectivity principle for airports connected by passenger traffic with each other.For the passenger traffic analysis the author took passenger traffic data by federal districts starting from the Far East. Then consequently the data for Siberian, Ural, Wolga, Northwestern, Central, Southern and North Caucasian federal districts were analyzed. Passenger traffic to the Crimea was treated separately. Detailed specifications of passenger traffics were provided in order to determine the connections between airports both within federal districts and beyond them and with neighboring areas in western direction. Query of routes was done based on limitations for non-stop flight range and on minimum and maximum (for significant traffics) flight frequencies.The analysis approach lets us concentrate attention on those airlines which at best fit for putting into service of chosen aircraft. Also this method permits to determine the routes with currently insufficient or low traffics but where there’s a definite growth potential. When analysis data are combined with traffic data and tariffs, then it becomes possible to determine the most profitable routes for introduction of new aircraft. Traffic volume, actual figures and forecast, consolidated characteristics of chosen airlines, list of airlines for further studies of efficiency and competitiveness of introduced aircraft are determined.

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Rahman, Muhammad Azizur, Algirdas Pakštas, and Frank Zhigang Wang. "Network modelling and simulation tools." Simulation Modelling Practice and Theory 17, no. 6 (July 2009): 1011–31. http://dx.doi.org/10.1016/j.simpat.2009.02.005.

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Smith, Stephen M., Karla L. Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F. Beckmann, Thomas E. Nichols, Joseph D. Ramsey, and Mark W. Woolrich. "Network modelling methods for FMRI." NeuroImage 54, no. 2 (January 2011): 875–91. http://dx.doi.org/10.1016/j.neuroimage.2010.08.063.

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Ho, Joshua W. K., and Michael A. Charleston. "Network modelling of gene regulation." Biophysical Reviews 3, no. 1 (December 23, 2010): 1–13. http://dx.doi.org/10.1007/s12551-010-0041-4.

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Craven, B. D. "Modelling inventories in a network." Optimization Letters 1, no. 4 (January 18, 2007): 401–6. http://dx.doi.org/10.1007/s11590-006-0040-3.

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Sadyrbaev, Felix, Inna Samuilik, and Valentin Sengileyev. "On Modelling of Genetic Regulatory Net Works." WSEAS TRANSACTIONS ON ELECTRONICS 12 (August 2, 2021): 73–80. http://dx.doi.org/10.37394/232017.2021.12.10.

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We consider mathematical model of genetic regulatory networks (GRN). This model consists of a nonlinear system of ordinary differential equations. The vector of solutions X(t) is interpreted as a current state of a network for a given value of time t: Evolution of a network and future states depend heavily on attractors of system of ODE. We discuss this issue for low dimensional networks and show how the results can be applied for the study of large size networks. Examples and visualizations are provided

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

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

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Russer, Peter. "Network methods in electromagnetic field modelling I: global modelling." International Journal of Applied Electromagnetics and Mechanics 17, no. 1-3 (June 5, 2003): 5–17. http://dx.doi.org/10.3233/jae-2003-257.

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Marcot, Bruce G., and Trent D. Penman. "Advances in Bayesian network modelling: Integration of modelling technologies." Environmental Modelling & Software 111 (January 2019): 386–93. http://dx.doi.org/10.1016/j.envsoft.2018.09.016.

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Dave, Kushal, Rushi Bhatt, and Vasudeva Varma. "Modelling Action Cascades in Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 121–28. http://dx.doi.org/10.1609/icwsm.v5i1.14092.

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The central idea in designing various marketing strategies for online social networks is to identify the influencers in the network. The influential individuals induce ``word-of-mouth" effects in the network. These individuals are responsible for triggering long cascades of influence that convince their peers to perform a similar action (buying a product, for instance). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. One way to measure an individual's influencing capability on its peers is by its reach for a certain action. We formulate identifying the influencers in a network as a problem of predicting the average depth of cascades an individual can trigger. We first empirically identify factors that play crucial role in triggering long cascades. Based on the analysis, we build a model for predicting the cascades triggered by a user for an action. The model uses features like influencing capabilities of the user and their friends, influencing capabilities of the particular action and other user and network characteristics. Experiments show that the model effectively improves the predictions over several baselines.

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Xiao, Lin, Fahui Wu, Dingcheng Yang, Tiankui Zhang, and Xiaoya Zhu. "Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3831810.

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The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has similarity to random networks of small average path length. It can be utilized to optimize the energy efficiency of the whole network. Optimal number of multiple sink nodes of the WSN topology is proposed for optimizing energy efficiency. Then, the hierarchical clustering analysis is applied to implement this clustering of the sensor nodes and pick up the sink nodes from the sensor nodes as the clustering head. Meanwhile, the update method is proposed to determine the sink node when the death of certain sink node happened which can cause the paralysis of network. Simulation results verify the energy efficiency of the proposed model and validate the updating of the sink nodes to ensure the normal operation of the WSN.

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Gulbinovič, Lech. "TECHNIQUES FOR MODELLING NETWORK SECURITY / KOMPIUTERIŲ SISTEMŲ SAUGUMO MODELIAVIMAS." Mokslas - Lietuvos ateitis 4, no. 1 (April 23, 2012): 27–30. http://dx.doi.org/10.3846/mla.2012.06.

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The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropriate. A real network distributes incidents referring to various distribution methods (exponential, gama, Veibul, etc.). A real system should evaluate time value when the stochastic activity network allows such activity. Santrauka Nagrinėjami šiuo metu žinomi kompiuterių sistemų saugumo modeliavimo metodai: tikimybių teorijos, Markovo procesų, Petri tinklų ir stochastinių veiklos tinklų. Šie metodai leidžia modeliuoti dinamines sistemas, kurioms kombinatoriniai metodai yra netinkami. Kadangi kompiuterių tinkluose įvykiai gali būti pasiskirstę ne tik pagal eksponentinį dėsnį, todėl jiems modeliuoti netinka Markovo procesų ir Petri tinklų modeliai. Realiuose tinkluose įvykiai gali būti pasiskirstę pagal eksponentinį, gama, Veibulo ir kt. dėsnius. Parodyta, kad iš aptartų šiuo metu žinomų modeliavimo metodų pagrindiniams saugumo veiksniams įvertinti tinkamiausias yra stochastinės veiklos tinklų metodas.

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Suárez Valencia, Erika, Víctor Bucheli, Roberto Zarama, and Ángel Garcia. "Collective intelligence: analysis and modelling." Kybernetes 44, no. 6/7 (June 1, 2015): 1122–33. http://dx.doi.org/10.1108/k-11-2014-0245.

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Purpose – The purpose of this paper is to focus on the underpinning dynamics that explain collective intelligence. Design/methodology/approach – Collective intelligence can be understood as the capacity of a collective system to evolve toward higher order complexity through networks of individual capacities. The authors observed two collective systems as examples of the dynamic processes of complex networks – the wiki course PeSO at the Universidad de Los Andes, Bogotá, Colombia, and an agent-based model inspired by wiki systems. Findings – The results of the wiki course PeSO and the model are contrasted with a random network baseline model. Both the wiki course and the model show dynamics of accumulation, in which statistical properties of non-equilibrium networks appear. Research limitations/implications – The work is based on network science. The authors analyzed data from two kinds of networks: the wiki course PeSO and an agent-based model. Limitations due to the number of computations and complexity appeared when there was a high order of magnitude of agents. Practical implications – Better understanding can allow for the measurement and design of systems based on collective intelligence. Originality/value – The results show how collective intelligence emerges from cumulative dynamics.

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Peterson, Erin E., Jay M. Ver Hoef, Dan J. Isaak, Jeffrey A. Falke, Marie-Josée Fortin, Chris E. Jordan, Kristina McNyset, et al. "Modelling dendritic ecological networks in space: an integrated network perspective." Ecology Letters 16, no. 5 (March 4, 2013): 707–19. http://dx.doi.org/10.1111/ele.12084.

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Thai, M. T., Z. Cai, and D. Z. Du. "Genetic networks: processing data, regulatory network modelling and their analysis." Optimization Methods and Software 22, no. 1 (February 2007): 169–85. http://dx.doi.org/10.1080/10556780600881860.

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Perestrelo, Sara, Maria C. Grácio, Nuno A. Ribeiro, and Luís M. Lopes. "Modelling Forest Fires Using Complex Networks." Mathematical and Computational Applications 26, no. 4 (September 28, 2021): 68. http://dx.doi.org/10.3390/mca26040068.

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Forest fires have been a major threat to the environment throughout history. In order to mitigate its consequences, we present, in a first of a series of works, a mathematical model with the purpose of predicting fire spreading in a given land portion divided into patches, considering the area and the rate of spread of each patch as inputs. The rate of spread can be estimated from previous knowledge on fuel availability, weather and terrain conditions. We compute the time duration of the spreading process in a land patch in order to construct and parametrize a landscape network, using cellular automata simulations. We use the multilayer network model to propose a network of networks at the landscape scale, where the nodes are the local patches, each with their own spreading dynamics. We compute some respective network measures and aim, in further work, for the establishment of a fire-break structure according to increasing accuracy simulation results.

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Bhattacharya, K., G. Mukherjee, J. Saramäki, K. Kaski, and S. S. Manna. "The International Trade Network: weighted network analysis and modelling." Journal of Statistical Mechanics: Theory and Experiment 2008, no. 02 (February 4, 2008): P02002. http://dx.doi.org/10.1088/1742-5468/2008/02/p02002.

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Sarkar, Nurul I., and Kashif Nisar. "Modelling and Performance Studies of ATM Networks Over Email & FTP." International Journal of Advanced Pervasive and Ubiquitous Computing 4, no. 2 (April 2012): 16–25. http://dx.doi.org/10.4018/japuc.2012040102.

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The Advanced Network Technologies is a research that investigates the technology(s) behind today’s modern networks and network infrastructures – one of these technologies being Asynchronous Transfer Mode (ATM); a technology commonly in place in networks all around the world today. Therefore, this paper focuses its attention on ATM. Dubbed “Modelling and Performance Studies of ATM Networks,” this research looks into the “impact of application segment length on the performance of an ATM network” and the “impact of traffic type data on the performance of an ATM network.” For this research, the authors have used the OPNET Modeler 14.0 Simulation software to create a network model that represents a real-life ATM network. By simulating an ATM network at AUT University New Zealand, the authors can change certain variables, and observe the effects the changes have on performance. As such, one of the impacts that will be explored is the effect that application segment length has on an ATM network. The second impact to be inspected is the impact of different traffic types on an ATM network. For example, Email traffic should theoretically affect an ATM network differently to the way that Voice traffic does. This way of thinking is what will be looked into further.

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Martynenko, A. V., and O. N. Ie. "Modelling of natural development of interurban network of vehicular roads." Herald of the Ural State University of Railway Transport, no. 2 (2020): 4–12. http://dx.doi.org/10.20291/2079-0392-2020-2-4-12.

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The main peculiarities of transport network functioning and structure are the result of reciprocal influence of the network and its service area. Namely, current transport needs of the area are the key factor, affecting any specific decision on creating a new network element. However, transport needs of the area significantly changes in the course of time and certain management decisions are poorly coordinated and made in conditions of incomplete information on future network state. Therefore, transport networks have a variety of features of natural objects and their development can be regarded as a result of implementation of some in-ner consistent patterns and mechanisms. The models of natural development of transport networks influenced by various external and internal factors are dealt with in the article. An approach to modelling of natural development of a transport network based on information on spatial location of its peaks is suggested. As an illustration of potentiality of the given approach Sverdlovsk region network of vehicular roads is used. A measure which indicates morphological similarity of the networks is introduced. The models of Sverdlovsk region vehicular road network are built for different representations of function, which characterize the extent of remoteness of one peak to the pair to other peaks. The generating network model which is sufficiently close to a real network is obtained. It is demonstrated that many fundamental features of a real network are the result of location of its peaks and for their explanation there is no need in additional social-economic and demographic information.

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P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling." March 2020 2, no. 1 (March 30, 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 (March 30, 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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Ji, Q., S. Barr, P. James, and D. Fairbairn. "A GEOSPATIAL ANALYSIS FRAMEWORK FOR FINE SCALE URBAN INFRASTRUCTURE NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 291–96. http://dx.doi.org/10.5194/isprs-archives-xlii-4-291-2018.

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Abstract. Understanding the spatial connectivity of urban infrastructure networks that connect assets to buildings is important for the fine-scale spatial analysis and modelling of the resource flows within cities. However, rarely are spatially explicit representations of infrastructure networks available for such analysis. Further, an appropriate database system is the core of development of an infrastructure assets information and management platform, capable of handling the wide range of data for infrastructure system modelling and analysis. In this paper, we develop a geospatial simulation and analysis framework, which is capable of generating fine-scale urban infrastructure networks and storing the network instances in a hybrid database system for further modelling and analysis needs. We demonstrate the use of this platform by simulating the entire-city electricity distribution network for the city of Newcastle upon Tyne. Validation of the resulting network is performed using the network layout diagram from the local power company. The heuristically derived network was found to have a 91% spatial accuracy.

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Journal articles on the topic 'Network modelling'

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