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Статті в журналах з теми "Complex Social Networks"
Gong, Yuhui, and Qian Yu. "Evolution of Conformity Dynamics in Complex Social Networks." Symmetry 11, no. 3 (February 28, 2019): 299. http://dx.doi.org/10.3390/sym11030299.
Повний текст джерелаXuan, Qi, Zhi-Yuan Zhang, Chenbo Fu, Hong-Xiang Hu, and Vladimir Filkov. "Social Synchrony on Complex Networks." IEEE Transactions on Cybernetics 48, no. 5 (May 2018): 1420–31. http://dx.doi.org/10.1109/tcyb.2017.2696998.
Повний текст джерелаCENTOLA, DAMON. "Failure in Complex Social Networks." Journal of Mathematical Sociology 33, no. 1 (December 30, 2008): 64–68. http://dx.doi.org/10.1080/00222500802536988.
Повний текст джерелаLópez-Pintado, Dunia. "Diffusion in complex social networks." Games and Economic Behavior 62, no. 2 (March 2008): 573–90. http://dx.doi.org/10.1016/j.geb.2007.08.001.
Повний текст джерелаLiu, Guanfeng, Yan Wang, and Mehmet Orgun. "Optimal Social Trust Path Selection in Complex Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1391–98. http://dx.doi.org/10.1609/aaai.v24i1.7509.
Повний текст джерелаLiu, Guanfeng, Yan Wang, and Mehmet Orgun. "Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 101–7. http://dx.doi.org/10.1609/aaai.v26i1.8114.
Повний текст джерелаAsbaş, Caner, Zühal Şenyuva, and Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success." Central European Management Journal 30, no. 1 (March 15, 2022): 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.
Повний текст джерелаYeqing, Zhao. "Knowledge Evolution of Complex Agent Networks." MATEC Web of Conferences 173 (2018): 03050. http://dx.doi.org/10.1051/matecconf/201817303050.
Повний текст джерелаYeqing, Zhao. "Knowledge Evolution of Complex Agent Networks." MATEC Web of Conferences 176 (2018): 03007. http://dx.doi.org/10.1051/matecconf/201817603007.
Повний текст джерелаLiu, Guanfeng, Yan Wang, and Mehmet Orgun. "Trust Transitivity in Complex Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1222–29. http://dx.doi.org/10.1609/aaai.v25i1.8087.
Повний текст джерелаДисертації з теми "Complex Social Networks"
Marchese, Emiliano. "Optimizing complex networks models." Thesis, IMT Alti Studi Lucca, 2022. http://e-theses.imtlucca.it/356/1/Marchese_phdthesis.pdf.
Повний текст джерелаUnicomb, Samuel Lee. "Threshold driven contagion on complex networks." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN003.
Повний текст джерелаNetworks arise frequently in the study of complex systems, since interactions among the components of such systems are critical. Net- works can act as a substrate for dynamical process, such as the diffusion of information or disease throughout populations. Network structure can determine the temporal evolution of a dynamical process, including the characteristics of the steady state. The simplest representation of a complex system is an undirected, unweighted, single layer graph. In contrast, real systems exhibit heterogeneity of interaction strength and type. Such systems are frequently represented as weighted multiplex networks, and in this work we in- corporate these heterogeneities into a master equation formalism in order to study their effects on spreading processes. We also carry out simulations on synthetic and empirical networks, and show that spread- ing dynamics, in particular the speed at which contagion spreads via threshold mechanisms, depend non-trivially on these heterogeneities. Further, we show that an important family of networks undergo reentrant phase transitions in the size and frequency of global cascades as a result of these interactions. A challenging feature of real systems is their tendency to evolve over time, since the changing structure of the underlying network is critical to the behaviour of overlying dynamical processes. We show that one aspect of temporality, the observed “burstiness” in interaction patterns, leads to non-monotic changes in the spreading time of threshold driven contagion processes. The above results shed light on the effects of various network heterogeneities, with respect to dynamical processes that evolve on these networks
Roth, Camille. "Co-evolution in epistemic networks : reconstructing social complex systems." Palaiseau, Ecole polytechnique, 2005. http://www.theses.fr/2005EPXX0057.
Повний текст джерелаAgents producing and exchanging knowledge are forming as a whole a socio-semantic complex system. Studying such knowledge communities offers theoretical challenges, with the perspective of naturalizing further social sciences, as well as practical challenges, with potential applications enabling agents to know the dynamics of the system they are participating in. The present thesis lies within the framework of this research program. Alongside and more broadly, we address the question of reconstruction in social science. Reconstruction is a reverse problem consisting of two issues: (i) deduce a given high-level observation for a considered system from low-level phenomena; and (ii) reconstruct the evolution of high-level observations from the dynamics of lower-level objects. In this respect, we argue that several significant aspects of the structure of a knowledge community are primarily produced by the co-evolution between agents and concepts, i. E. The evolution of an epistemic network. In particular, we address the first reconstruction issue by using Galois lattices to rebuild taxonomies of knowledge communities from low-level observation of relationships between agents and concepts; achieving ultimately an historical description (inter alia field progress, decline, specialization, interaction - merging or splitting). We then micro-found various stylized facts regarding this particular structure, by exhibiting processes at the level of agents accounting for the emergence of epistemic community structure. After assessing the empirical interaction and growth processes, and assuming that agents and concepts are co-evolving, we successfully propose a morphogenesis model rebuilding relevant high-level stylized facts. We finally defend a general epistemological point related to the methodology of complex system reconstruction, eventually supporting our choice of a co-evolutionary framework
Savoy, Daniel Prata. "A dinâmica de opinião dos debates públicos em redes sociais complexas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-04022013-114700/.
Повний текст джерелаThis work studies the effects caused by variations in network topology in the behavior of four different models of opinion dynamics: the Voter Model, Bounded Confidence Model, the Majority Rule Model and the CODA Model. First, these models are used in simulations over a number of different complex social networks, generated to show sereval combinations of key properties such as clustering, connectivity, assortativity and path distances. Then, we perform experiments that show how the topology influences the results in modeling scenarios of public debates, where two rival opinions, A and B, compete under unequal conditions for the consensus of a simulated population.
Ciotti, Valerio. "Positive and negative connections and homophily in complex networks." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31787.
Повний текст джерелаAbreu, Luís Fernando Dorelli de. "Estrutura e dinâmica de redes de informação." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08112016-091004/.
Повний текст джерелаThe raise in the availability of data regarding interactions between people online has opened new doors to study the process of information diffusion in social networks. In this present work, we make use of the data from the micro-blogging website Twitteralong with complex networks concepts to understand, characterize and classify information diffusion processes observed in this platform and in social networks in general. We present important measures to characterize information cascades and efficient algorithms to calculate them. With the help of these measures, we show that it is possible to quantify the influence of the social network in the process of information diffusion. After that, we show that information does tend to travel along shortest paths on Twitter. Finally, we show that the topology of the social network, without any extra semantic information, can be used to aggregate topics, and that such topology is highly influenced by the topics being discussed on it. Altough we work with only a single dataset, our methods and measures developed are general and can be applied to any process of information diffusion and any complex network.
Libardi, Paula Luciene Oliveira 1980. "Detecção computacional de falecidos em redes sociais online." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267725.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia
Made available in DSpace on 2018-08-27T04:53:50Z (GMT). No. of bitstreams: 1 Libardi_PaulaLucieneOliveira_M.pdf: 1610224 bytes, checksum: a08b75cd1a30c421927617ee8b6ac8d4 (MD5) Previous issue date: 2015
Resumo: A identificação de usuários falecidos em Redes Sociais Online é um desafio em aberto e, dado o tamanho das principais redes, abordagens que envolvam intervenção manual são impraticáveis. Usuários inativos por longo tempo inviabilizam soluções simples tais como a expiração de um prazo desde o último acesso, o que torna difícil a diferenciação entre inativos e falecidos. Esta pesquisa iniciou-se com o pressuposto de que o problema poderia ser parcialmente resolvido com métodos automáticos e a hipótese era de que dois métodos aqui propostos, um baseado na análise de frequência de mensagens trocadas entre usuários e outro fundamentado na combinação de informações da topologia da rede junto a inspeções de mensagens, poderiam identificar satisfatoriamente parte dos usuários falecidos. Para testar esta hipótese, recorreu-se à simulação computacional, usando topologias livre de escala e aleatória. O programa que simula as redes foi construído de forma a aplicar e testar os métodos de identificação de falecidos, seguindo padrões de projeto que permitem facilmente a troca ou o encadeamento dos algoritmos a validar. Dessa característica, originou-se um terceiro método, que é a combinação das saídas de algoritmos detectores aplicados anteriormente à rede. Os resultados da pesquisa validaram a hipótese, sendo que os dois métodos propostos inicialmente tiveram, cada qual, índices de acerto superiores a 70% na maioria dos casos simulados, independentemente da topologia da rede. Em ambos os métodos, no entanto, é necessária uma calibração de dois parâmetros operacionais, o que exige algum conhecimento da rede examinada e influencia na taxa de detecção. O último método mostrou-se bastante eficiente, com detecção correta superior a 94%, e capaz de absorver flutuações na taxa de detecção dos demais métodos advindas de suas respectivas parametrizações. Portanto, os objetivos da pesquisa foram plenamente atingidos, com a validação da hipótese inicial, a proposta de três métodos para a solução do problema e a geração de um produto tecnológico, o Demortuos, que é o software de simulação da rede e teste dos métodos, atualmente em processo de registro no Instituto Nacional da Propriedade Industrial (INPI). Adicionalmente, foram abertas possibilidades para o desenvolvimento de métodos automáticos para busca de outras classes de usuários
Abstract: Identifying deceased users in Online Social Networks is an open challenge and, given the size of the main networks, approaches involving manual intervention are impractical. Inactive users for a long time prevent simple solutions such as the expiration of a period since the last entry, making it difficult to differentiate between inactive and deceased users. This research began with the assumption that the problem could be partially solved with automated methods and the hypothesis was that two methods proposed here, one based on frequency analysis of messages exchanged between users and the other based on the combination of topology information network with the messages of inspections, could satisfactorily identify the part of deceased users. To test this hypothesis, we used the computer simulation, using free topologies of scale and random, the latter for comparison purposes. The program that simulates the network was constructed to implement and test the deceased identification methods, following design patterns that easily allow the exchange or the chain of algorithms to validate. This characteristic gave up a third method, which is combining the outputs of detectors algorithms previously applied to the network. The survey results validated the hypothesis, and the two proposed methods initially had, each, hit rates of over 70% in most cases simulated, regardless of the network topology. In both methods, however, two operating parameters calibration is necessary, which requires some knowledge of the network and examined influences the detection rate. The last method proved to be very efficient with proper detection above 94%, and able to absorb fluctuations in the detection rate of other methods resulting from their respective parameterization. Therefore, the research objectives were fully achieved, with the validation of the initial hypothesis, the proposed three methods for the solution of the problem and the generation of a technological product, Demortuos, which is the network simulation software and testing methods currently in the registration process at the National Institute of Industrial Property (INPI). Moreover, possibilities are opened for the development of automated methods to search for other classes of users
Mestrado
Tecnologia e Inovação
Mestra em Tecnologia
Grabowicz, Przemyslaw Adam. "Complex networks approach to modeling online social systems. The emergence of computational social science." Doctoral thesis, Universitat de les Illes Balears, 2014. http://hdl.handle.net/10803/131220.
Повний текст джерелаLa presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales.
Grando, Felipe. "On the analysis of centrality measures for complex and social networks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/122516.
Повний текст джерелаOver the last years, centrality measures have gained importance within complex and social networks research, e.g., as predictors of behavior, identification of powerful and influential elements, detection of critical spots in communication networks and in transmission of diseases. New measures have been created and old ones reinvented, but few have been proposed to understand the relation among measures as well as between measures and other structural properties of the networks. Our research analyzes and studies these relations with the objective of providing a guide to the application of existing centrality measures for new environments and new purposes. We shall also present evidence that the measures known as Walk Betweenness, Information, Eigenvector and Betweenness are substantially better than other metrics in distinguishing vertices in a network by their structural properties. Furthermore, we provide evidence that each metric performs better with respect to distinct kinds of networks. In addition, we show that most metrics present a high level of redundancy (over 0.8 correlation) and its simultaneous use, in most cases, is fruitless. The results achieved in our research reinforce the idea that to use centrality measures properly, knowledge about their underlying properties and behavior is valuable, as we show in this dissertation.
Amiri, Babak. "Evolutionary Algorithms for Community Detection in Complex Networks." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10451.
Повний текст джерелаКниги з теми "Complex Social Networks"
Savić, Miloš, Mirjana Ivanović, and Lakhmi C. Jain. Complex Networks in Software, Knowledge, and Social Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91196-0.
Повний текст джерелаShen, Hua-Wei. Community Structure of Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Знайти повний текст джерелаPardalos, P. M. (Panos M.), 1954- and SpringerLink (Online service), eds. Handbook of Optimization in Complex Networks: Theory and Applications. Boston, MA: Springer US, 2012.
Знайти повний текст джерелаGlattfelder, James B. Decoding Complexity: Uncovering Patterns in Economic Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Знайти повний текст джерела1960-, Deutsch Andreas, Mukherjee Animesh, and SpringerLink (Online service), eds. Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences. Boston: Birkhäuser Boston, 2009.
Знайти повний текст джерелаAndrew, Tait, and Richardson Kurt A, eds. Complexity and knowledge management: Understanding the role of knowledge in the management of social networks. Charlotte, NC: Information Age Pub., 2010.
Знайти повний текст джерелаF, Mendes J. F., and Fundação para a Ciência e a Tecnologia., eds. Science of complex networks: From biology to the Internet and WWW : CNET 2004 : Aveiro, Portugal, 29 August-2 September, 2004. Melville, N.Y: American Institute of Physics, 2005.
Знайти повний текст джерелаKarampelas, Panagiotis. Techniques and Tools for Designing an Online Social Network Platform. Vienna: Springer Vienna, 2013.
Знайти повний текст джерела1953-, Kurths J., and Zhou Changsong, eds. Synchronization in oscillatory networks. Berlin: Springer, 2007.
Знайти повний текст джерелаGuide complet des réseaux sociaux. Paris: First interactive, 2013.
Знайти повний текст джерелаЧастини книг з теми "Complex Social Networks"
Barbosa Filho, Hugo S., Fernando B. Lima Neto, and Wilson Fusco. "Migration, Communication and Social Networks – An Agent-Based Social Simulation." In Complex Networks, 67–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9_8.
Повний текст джерелаCollingsworth, Ben, and Ronaldo Menezes. "Identification of Social Tension in Organizational Networks." In Complex Networks, 209–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01206-8_18.
Повний текст джерелаBonato, Anthony, and Yanhua Tian. "Complex Networks and Social Networks." In Mathematics in Industry, 269–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30904-5_12.
Повний текст джерелаVenugopal, Srividhya, Evan Stoner, Martin Cadeiras, and Ronaldo Menezes. "The Social Structure of Organ Transplantation in the United States." In Complex Networks, 199–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9_21.
Повний текст джерелаSlingerland, Willeke. "Social Capital, Corrupt Networks, and Network Corruption." In Understanding Complex Systems, 9–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81484-7_2.
Повний текст джерелаZhang, W., C. Lim, and B. Szymanski. "Tipping Points of Diehards in Social Consensus on Large Random Networks." In Complex Networks, 161–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9_17.
Повний текст джерелаAswad, Firas, Harith Hamoodat, Eraldo Ribeiro, and Ronaldo Menezes. "Communities of Human Migration in Social Media: An Experiment in Social Sensing." In Complex Networks XI, 222–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40943-2_19.
Повний текст джерелаMontoya, Leydi Viviana, Athen Ma, and Raúl J. Mondragón. "Social Achievement and Centrality in MathOverflow." In Complex Networks IV, 27–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8_3.
Повний текст джерелаHinrichsen, Haye, Tobias Hoßfeld, Matthias Hirth, and Phuoc Tran-Gia. "Entropy Production in Stationary Social Networks." In Complex Networks IV, 47–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8_5.
Повний текст джерелаMitzlaff, Folke, Martin Atzmueller, Gerd Stumme, and Andreas Hotho. "Semantics of User Interaction in Social Media." In Complex Networks IV, 13–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8_2.
Повний текст джерелаТези доповідей конференцій з теми "Complex Social Networks"
Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.
Повний текст джерелаPeter, Ueli, and Tomas Hruz. "Clustering Signature in Complex Social Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.273.
Повний текст джерелаBhatt, Sujay, and Tamer Basar. "Streisand Games on Complex Social Networks." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303945.
Повний текст джерелаSantos, Eunice E., Eugene Santos, John T. Wilkinson, John Korah, Keumjoo Kim, Deqing Li, and Fei Yu. "Modeling complex social scenarios using Culturally Infused Social Networks." In 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2011. http://dx.doi.org/10.1109/icsmc.2011.6084158.
Повний текст джерелаWehmuth, K., and A. Ziviani. "Distributed Assessment of Network Centralities in Complex Social Networks." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.181.
Повний текст джерелаSvenson, Pontus. "Complex networks and social network analysis in information fusion." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301554.
Повний текст джерелаFelix, Lucas G. S., Carlos M. Barbosa, Vinícius da F. Vieira, and Carolina Ribeiro Xavier. "A Social Network Analysis of Football with Complex Networks." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8134.
Повний текст джерелаZhang, Yu, and Jason Leezer. "Emergence of Social Norms in Complex Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.392.
Повний текст джерелаZhang, Hao Lan, Jiming Liu, Chunyu Feng, Chaoyi Pang, Tongliang Li, and Jing He. "Complex social network partition for balanced subnetworks." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727744.
Повний текст джерелаLiang, Tianxin, Xiaoping Yang, Liang Wang, and Zhenyuan Han. "Kinship Determination in Mobile Social Networks." In 2018 23rd International Conference on Engineering of Complex Computer Systems (ICECCS). IEEE, 2018. http://dx.doi.org/10.1109/iceccs2018.2018.00033.
Повний текст джерелаЗвіти організацій з теми "Complex Social Networks"
Водолєєва, І. С., А. О. Лазаренко та В. М. Соловйов. Дослідження стійкості мультиплексних мереж під час кризових явищ. Видавець Вовчок О.Ю., 2017. http://dx.doi.org/10.31812/0564/1259.
Повний текст джерелаMcKenna, Patrick, and Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, June 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
Повний текст джерелаDavies, Will. Improving the engagement of UK armed forces overseas. Royal Institute of International Affairs, January 2022. http://dx.doi.org/10.55317/9781784135010.
Повний текст джерелаСоловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
Повний текст джерелаSoloviev, Vladimir, Natalia Moiseienko, and Olena Tarasova. Modeling of cognitive process using complexity theory methods. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3609.
Повний текст джерелаKokurina, O. Yu. VIABILITY AND RESILIENCE OF THE MODERN STATE: PATTERNS OF PUBLIC-LEGAL ADMINISTRATION AND REGULATION. Kokurina O.Yu., February 2022. http://dx.doi.org/10.12731/kokurina-21-011-31155.
Повний текст джерелаYatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.
Повний текст джерелаСоловйов, Володимир Миколайович, Наталя Володимирівна Моісеєнко, and Олена Юріївна Тарасова. Complexity theory and dynamic characteristics of cognitive processes. Springer, January 2020. http://dx.doi.org/10.31812/123456789/4143.
Повний текст джерелаPererva, Victoria V., Olena O. Lavrentieva, Olena I. Lakomova, Olena S. Zavalniuk, and Stanislav T. Tolmachev. The technique of the use of Virtual Learning Environment in the process of organizing the future teachers' terminological work by specialty. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3868.
Повний текст джерелаMarsden, Eric, Noëlle Laneyrie, Cécile Laugier, and Olivier Chanton. La relation contrôleur-contrôlé au sein d’un réseau d’acteurs. Fondation pour une culture de sécurité industrielle, June 2023. http://dx.doi.org/10.57071/933rrr.
Повний текст джерела