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Journal articles on the topic 'Social networks – Mathematical models'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

Anderson, Brian D. O., and Mengbin Ye. "Mathematical Models of Self-Appraisal in Social Networks." Journal of Systems Science and Complexity 34, no. 5 (2021): 1604–33. http://dx.doi.org/10.1007/s11424-021-1193-y.

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Jelassi, Mariem, Kayode Oshinubi, Mustapha Rachdi, and Jacques Demongeot. "Epidemic dynamics on social interaction networks." AIMS Bioengineering 9, no. 4 (2022): 348–61. http://dx.doi.org/10.3934/bioeng.2022025.

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<abstract> <p>The present paper aims to apply the mathematical ideas of the contagion networks in a discrete dynamic context to the modeling of two current pandemics, i.e., COVID-19 and obesity, that are identified as major risks by the World Health Organization. After providing a reminder of the main tools necessary to model epidemic propagation in a Boolean framework (Hopfield-type propagation equation, notion of centrality, existence of stationary states), we present two applications derived from the observation of real data and involving mathematical models for their interpreta

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Lavenant, H., and B. Maury. "Opinion propagation on social networks: a mathematical standpoint." ESAIM: Proceedings and Surveys 67 (2020): 285–335. http://dx.doi.org/10.1051/proc/202067016.

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These lecture notes address mathematical issues related to the modeling of opinion propagation on networks of the social type. Starting from the behavior of the simplest discrete linear model, we develop various standpoints and describe some extensions: stochastic interpretation, monitoring of a network, time continuous evolution problem, charismatic networks, links with discretized Partial Differential Equations, nonlinear models, inertial version and stability issues. These developments rely on basic mathematical tools, which makes them accessible at an undergraduate level. In a last section

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Assunção, Diana, Isabel Pedrosa, Rui Mendes, et al. "Social Network Analysis: Mathematical Models for Understanding Professional Football in Game Critical Moments—An Exploratory Study." Applied Sciences 12, no. 13 (2022): 6433. http://dx.doi.org/10.3390/app12136433.

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Considering the Social Network Analysis approach and based on the creation of mathematical models, the aim of this study is to analyze the players’ interactions of professional football teams in critical moments of the game. The sample consists in the analysis of a 2019/2020 season UEFA Champions League match. The mathematical models adopted in the analysis of the players (micro analysis) and the game (macro analysis) were obtained through the uPATO software. The results of the networks indicated a performance pattern trend more robust in terms of the mathematical model: Network Density. As fa

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Bonato, Anthony, Noor Hadi, Paul Horn, Paweł Prałat, and Changping Wang. "Models of Online Social Networks." Internet Mathematics 6, no. 3 (2009): 285–313. http://dx.doi.org/10.1080/15427951.2009.10390642.

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

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

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Mikołajczak, Rafał, Łukasz Mikołajczyk, Paulina Wojtyła-Buciora, Anna Królak, and Hanna Krauss. "The Use of Social Networks in Epidemic Modeling." Biuletyn Głównej Biblioteki Lekarskiej 56, no. 381 (2023): 123–42. https://doi.org/10.2478/bgbl-2023-0018.

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Abstract Mathematical modeling used in epidemiology enables infection scenarios in given populations to be partially predicted. The data obtained from such simulations, based on these models, not only permit the possible course of events to observed, but also allow an understanding of the complex dynamics of the epidemic. Understanding the threat, allows in-place mitigation strategies to be systematically evaluated such as quarantine, drug distribution, screening and vaccination.

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Saunders, Clare. "Unblocking the Path to Effective Block Modeling in Social Movement Research." Mobilization: An International Quarterly 16, no. 3 (2011): 283–302. http://dx.doi.org/10.17813/maiq.16.3.a70276715p171144.

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Key studies of social movement networks use block modelling to uncover movement network structures. While it is promising to see mathematical sociology techniques applied here, there are grounds for engendering an even closer connection between these two fields of study. The mathematical sociology literature recommends, for example, that analyzed networks should be complete and relatively dense, that some degree of deduction should be applied to select the "best" model, that levels of equivalence and/or error scores should be specified, and that reliable and appropriate algorithms and levels o

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Govindankutty, Sreeraag, and Shynu Padinjappurathu Gopalan. "SEDIS—A Rumor Propagation Model for Social Networks by Incorporating the Human Nature of Selection." Systems 11, no. 1 (2022): 12. http://dx.doi.org/10.3390/systems11010012.

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The explosive evolution of the internet has paved the path for the rise of social networks, which can help people connect remotely. Currently, social networks are commonly used for sharing thoughts, feelings, information, and personal life, which vary from individual to individual. The world has witnessed a tremendous increase in social media usage in the last decade, and more people are expected to spend their time online after the COVID-19 pandemic. This increases the rapid propagation of rumors and fake news within societies and communities. On one end, social networks act as an excellent p

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Gabdrakhmanova, Nailia, and Maria Pilgun. "Intelligent Control Systems in Urban Planning Conflicts: Social Media Users’ Perception." Applied Sciences 11, no. 14 (2021): 6579. http://dx.doi.org/10.3390/app11146579.

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The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the North-Eastern Chord (NEC) in Moscow (RF). To analyze the content of social media, a multimodal approach was used. The paper presents the resu

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Herdt, Rudolf, Louisa Kinzel, Johann Georg Maaß, et al. "Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models." Journal of the Acoustical Society of America 156, no. 4 (2024): 2448–66. http://dx.doi.org/10.1121/10.0030473.

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Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed at automating both the quantitative (detection) and qualitative (classification) analysis of USVs. So far, no notable efforts have been made to determine the most suitable architecture. We present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, in

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Odlyzko, Andrew. "Social networks and mathematical models: A research commentary on “Critical Mass and Willingness to Pay for Social Networks” by J. Christopher Westland." Electronic Commerce Research and Applications 9, no. 1 (2010): 26–28. http://dx.doi.org/10.1016/j.elerap.2009.11.007.

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Senad, Orhani. "Use of Mathematical Models in Epidemiology to Predict Infectious." Partners Universal Multidisciplinary Research Journal (PUMRJ) 01, no. 04 (2024): 96–111. https://doi.org/10.5281/zenodo.14208781.

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Mathematical models play a key role in epidemiology, providing a powerful tool for predicting and controlling the spread of infectious diseases. This paper examines the use of mathematical models to analyze the dynamics of infectious diseases, assess the impact of health interventions, and predict future outbreaks. Initially, the structure of basic models such as SIR (Susceptible, Infected, Recovered) and their modifications to take into account factors such as population heterogeneity, social networks, and seasonal changes will be discussed. Next, model parameterization and calibration techni

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Kopanov, Peter, and Ivan Tchalakov. "‘Stacked' Actor-Networks and Their Computer Modelling." International Journal of Actor-Network Theory and Technological Innovation 8, no. 4 (2016): 52–69. http://dx.doi.org/10.4018/ijantti.2016100104.

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The paper dev?lops the mathematical basis of stacked actor-network (SAN) approach in modeling a socio-economic and cultural dynamics. It attempts to avoid the idea of ‘guidance' of a given form of (social) life being modelled, which has long been dominating in traditional sociology. Using methods of discreet mathematics and stochastic finite automata approach, we provided initial mathematical formalization of agent and actor-network, the types of complexity in the actor-network and three basic types of graphs comprising SAN's minimal model, with further aim is to develop these models in the fo

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Zoller, J., and S. Montangero. "Probing models of information spreading in social networks." Journal of Physics A: Mathematical and Theoretical 47, no. 43 (2014): 435102. http://dx.doi.org/10.1088/1751-8113/47/43/435102.

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Vasylieva, Olha. "MODELS FOR DETECTING AND FORECASTING THE DYNAMICS OF INFORMATION OPERATIONS IN SOCIAL NETWORKS." Technical Sciences and Technologies, no. 2(32) (2023): 193–200. http://dx.doi.org/10.25140/2411-5363-2023-2(32)-193-200.

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The article examines some provisions for conducting information operations as one of the main threats to national secu-rity in the information sphere, demonstrates the use of social networks as the main field for conducting them thanks to the latest technologies, and analyzes the proposed models of information operations in social networks.The article is devoted to the problem of the complex application of theoretical models used in the study of "viral" infor-mation dissemination in social networks of cyberspace.Formal modeling, which is used to study the peculiarities of communication in soci

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Elgazzar, Ahmed S. "Simple mathematical models for controlling COVID-19 transmission through social distancing and community awareness." Zeitschrift für Naturforschung C 76, no. 9-10 (2021): 393–400. http://dx.doi.org/10.1515/znc-2021-0004.

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Abstract The novel COVID-19 pandemic is a current, major global health threat. Up till now, there is no fully approved pharmacological treatment or a vaccine. Also, its origin is still mysterious. In this study, simple mathematical models were employed to examine the dynamics of transmission and control of COVID-19 taking into consideration social distancing and community awareness. Both situations of homogeneous and nonhomogeneous population were considered. Based on the calculations, a sufficient degree of social distancing based on its reproductive ratio is found to be effective in controll

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Marinosyan, Andreas Kh. "Directions for the Development of Social Sciences and Humanities in the Context of Creating Artificial General Intelligence." Russian Journal of Philosophical Sciences 66, no. 4 (2023): 26–51. http://dx.doi.org/10.30727/0235-1188-2023-66-4-26-51.

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The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It is projec

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Tsocheva, Ksenia Ivova. "Mathematical Analysis of Some Reaction Networks Inducing Biological Growth/Decay Functions." Biomath Communications 7, no. 1 (2020): 14. http://dx.doi.org/10.11145/bmc.2020.07.067.

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In this work, we study some characteristics of sigmoidal growth/decay functions that are solutions of dynamical systems. In addition, the studied dynamical systems have a realization in terms of reaction networks that are closely related to the Gompertzian and logistic type growth models. Apart from the growing species, the studied reaction networks involve an additional species interpreted as an environmental resource. The reaction network formulation of the proposed models hints for the intrinsic mechanism of the modeled growth process and can be used for analyzing evolutionary measured data

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Radheshyam R Sharma. "Optimizing Graph Algorithms for Large-Scale Networks: A Mathematical Framework." Communications on Applied Nonlinear Analysis 32, no. 8s (2025): 763–72. https://doi.org/10.52783/cana.v32.3807.

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A mathematical framework for optimising graph algorithms for large-scale networks is presented in this paper. The effectiveness and scalability of classical graph algorithms are severely hampered by the growing complexity and scale of networks. This study presents new optimisation strategies, such as graph partitioning, heuristics, and parallel computing tactics, that improve algorithm performance using sophisticated mathematical models. The framework exhibits enhanced computing efficiency and decreased processing time for a variety of graph-related activities, including network flows, connect

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Tamura, Kohei, Yutaka Kobayashi, and Yasuo Ihara. "Evolution of individual versus social learning on social networks." Journal of The Royal Society Interface 12, no. 104 (2015): 20141285. http://dx.doi.org/10.1098/rsif.2014.1285.

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A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to

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Chernyaev, Aleksandr A., and Aleksandr G. Ivashko. "Mathematical modeling of the assessment of credibility in a message in social networks on Russian language." Journal Of Applied Informatics 18, no. 4 (2023): 121–32. http://dx.doi.org/10.37791/2687-0649-2023-18-4-121-132.

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The problem of unreliable information is currently the most critical in the field of information dissemination in the Internet environment. The global transition of information sources to the Internet has led to the fact that information is distributed too quickly, and it is quite difficult to verify the accuracy of the information. This topic is raised when talking about the media, social networks, blogs, and other sources of information. The transmission of information has ceased to be a matter only for the media. Any Internet user can be a source of information. The development of free sour

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Adams, Johnathan A., Gentry White, and Robyn P. Araujo. "Mathematical measures of societal polarisation." PLOS ONE 17, no. 10 (2022): e0275283. http://dx.doi.org/10.1371/journal.pone.0275283.

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In opinion dynamics, as in general usage, polarisation is subjective. To understand polarisation, we need to develop more precise methods to measure the agreement in society. This paper presents four mathematical measures of polarisation derived from graph and network representations of societies and information-theoretic divergences or distance metrics. Two of the methods, min-max flow and spectral radius, rely on graph theory and define polarisation in terms of the structural characteristics of networks. The other two methods represent opinions as probability density functions and use the Ku

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Akhramovych, Volodymyr. "Method of calculating of the information in social networks protection depending on the number of communities." Collection "Information Technology and Security" 11, no. 1 (2023): 15–26. http://dx.doi.org/10.20535/2411-1031.2023.11.1.279868.

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A mathematical model (linear system of differential equations) was developed and a research of the model of personal data protection against the number of communities and the intensity of data transfer in social networks was conducted. The linear system of information protection in social networks in the mathematical sense of this term is considered. When described by linear models, the object should be linear, at least approximately. This approach makes it quite simple to consider mathematical models. If such a thing is not noticed, it is necessary to examine the security system for linearity

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Wang, Shuo. "Application of Graph Theory in Social Network Analysis." Theoretical and Natural Science 79, no. 1 (2025): 173–79. https://doi.org/10.54254/2753-8818/2025.20135.

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This literature review aims to discuss the application of graph theory in analyzing social and information networks. We first introduce some key network properties, such as clustering coefficient (transitivity), centrality, and diameter, which are crucial for understanding the dynamics of information dissemination within networks. Then, we talk about, based on these properties, how graph theory can be utilized to analyze social and information networks. Lastly, we provide an overview of various fundamental social and information models including the SIR model and the Linear Threshold model. Fo

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Kuzmenko, O., H. Yarovenko, and L. Skrynka. "ANALYSIS OF MATHEMATICAL MODELS FOR COUNTERING CYBER FRAUD IN BANKS." Vìsnik Sumsʹkogo deržavnogo unìversitetu 2022, no. 2 (2022): 111–20. http://dx.doi.org/10.21272/1817-9215.2022.2-13.

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The article is devoted to the current topic of analysis of mathematical models for countering cyber fraud in banks. This problem is due to the security risks growth in the banking system, which are formed by fraudsters' cyberattacks and cybercrimes implementation. Therefore, the priority task for cyberbanking security is the application of modern mathematical methods to analyse the sources of cyber attacks, identify threats and losses in the banking services market, identify cyber-attacks and assess the scenario of potential cyber risk, etc. The article analyses the most widespread types of cy

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Assel, Imanberdi, Lira La, Aitolkyn Kulmuratova, et al. "Assessment of the main features of the model of dissemination of information in social networks." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6729–36. https://doi.org/10.11591/ijece.v13i6.pp6729-6736.

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Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information disseminat

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Le, Van-Vang, Toai Kim Tran, Bich-Ngan T. Nguyen, Quoc-Dung Nguyen, and Vaclav Snasel. "Network Alignment across Social Networks Using Multiple Embedding Techniques." Mathematics 10, no. 21 (2022): 3972. http://dx.doi.org/10.3390/math10213972.

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Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this p

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Nanda, Sikata, Sadhu Charan Mahapatra, and Anshuman Dash. "Unravelling the web of tuberculosis: Mathematical models to decode and defeat tuberculosis transmission complexity." Journal of Community Health Management 10, no. 3 (2023): 118–20. http://dx.doi.org/10.18231/j.jchm.2023.027.

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In the transmission of infectious illnesses like tuberculosis, mathematical models are crucial in public health. The fundamental paradigm is compartmental modelling, which classifies people into categories such as susceptible, latent, active, and recovered. Extensions and variations in this model can be utilized to capture intricate details. To research TB transmission dynamics, structural models including age-structured, agent-based, stochastic, SEIR, spatial, drug-resistant, vaccination, contact tracking, and treatment models are employed. Populations are divided into age groups by age-struc

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Divak, M. P., A. M. Melnyk, Ye S. Kedrin, and Frank Avalon Otoo. "Interval model of the portrait of users of the thematic group on environmental issues in the social network." Optoelectronic Information-Power Technologies 41, no. 1 (2022): 78–88. http://dx.doi.org/10.31649/1681-7893-2021-41-1-78-88.

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Mathematical models of dynamics of efficiency of information social networks are considered in the work. An approach to estimating model parameters is proposed. A number of experimental studies were conducted on the basis of data on the functioning of a special online group Facebook. The indicator of the characteristics of the information message was studied. An interval discrete model in the form of a difference equation is obtained, which describes the dynamics of users' reactions to messages in thematic groups of social networks. On the basis of the conducted experiments, the efficiency of

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Yap, Hock Yeow, and Tong-Ming Lim. "Social trust: impacts on social influential diffusion." International Journal of Web Information Systems 13, no. 2 (2017): 199–219. http://dx.doi.org/10.1108/ijwis-11-2016-0067.

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Purpose This paper aims to present social trust as a variable of influence by demonstrating the possibilities of trusted social nodes to improve influential capability and rate of successfully influenced social nodes within a social networking environment. Design/methodology/approach This research will be conducted using simulated experiments. The base algorithm in research uses genetics algorithm diffusion model (GADM) where it carries out social influence calculations within a social networking environment. The GADM algorithm will be enhanced by integrating trust values into its influential

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Yevseiev, Serhii, Oleksandr Laptiev, Sergii Lazarenko, Anna Korchenko, and Iryna Manzhul. "MODELING THE PROTECTION OF PERSONAL DATA FROM TRUST AND THE AMOUNT OF INFORMATION ON SOCIAL NETWORKS." EUREKA: Physics and Engineering, no. 1 (January 29, 2021): 24–31. http://dx.doi.org/10.21303/2461-4262.2021.001615.

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The article analyzes the parameters of social networks. The analysis is performed to identify critical threats. Threats may lead to leakage or damage to personal data. The complexity of this issue lies in the ever-increasing volume of data. Analysts note that the main causes of incidents in Internet resources are related to the action of the human factor, the mass hacking of IoT devices and cloud services. This problem is especially exacerbated by the strengthening of the digital humanistic nature of education, the growing role of social networks in human life in general. Therefore, the issue

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Luo, Peng, Chong Wu, and Yongli Li. "Link prediction measures considering different neighbors’ effects and application in social networks." International Journal of Modern Physics C 28, no. 03 (2017): 1750033. http://dx.doi.org/10.1142/s0129183117500334.

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Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link

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Tsintsaris, Dimitris, Milan Tsompanoglou, and Evangelos Ioannidis. "Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business." Mathematics 12, no. 8 (2024): 1141. http://dx.doi.org/10.3390/math12081141.

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In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and Knowledge Dynamics (KD). In the OD case, we highlight special classes of social agents, such as informed agents, contrarians and extremists. As regards GDM, we present state-of-the-art models on various kinds of decision-making processes. In the KD case, we discuss processes of knowledge diffusion and

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Bakır, Çiğdem, and Kübra Öngenli. "Prediction and Comparative Analysis of Factors Affecting the Mathematical Achievement of Gifted Students With Machine Learning Models." International Journal of Adult Education and Technology 16, no. 1 (2025): 1–25. https://doi.org/10.4018/ijaet.383672.

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This study aims to determine the factors affecting the mathematical achievement of gifted students studying at science and art centers in Bursa province and to predict this achievement using various machine learning models. In the study, variables, such as demographic information, family structure, study habits, motivation level, technology use, and social activities were analyzed in line with the data collected from 151 students. Methods, such as decision trees, support vector machines, and artificial neural networks, were used by utilizing the fields of educational data mining and learning a

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Mikhailova, Svetlana Viktorovna. ""Fakes" in social networking media and modeling "fake infection"." Personality & Society 2, no. 4 (2021): 4–10. http://dx.doi.org/10.46502/issn.2712-8024/2021.4.1.

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The growth of dynamism, the complexity of relationships in social networks requires a systematic approach, the development of mathematical models for forecasting and the identification of fake news in social networks. Otherwise, it is difficult to resist media misinformation, fake news. The problem is urgent, there are more and more opportunities for exchanging "viral" and fake messages in social networks, and we poorly implement monitoring, identifying fake risks. Social networks so far do not allow reliably distinguishing lies from news from aggregator. The purpose of the work is to predict

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Bellingeri, Michele, Daniele Bevacqua, Francesco Scotognella, and Davide Cassi. "The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective." Mathematics 12, no. 6 (2024): 792. http://dx.doi.org/10.3390/math12060792.

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This review underscores the critical significance of incorporating networks science in epidemiology. Classic mathematical compartmental models (CMs) employed to describe epidemic spreading may fail to capture the intricacies of real disease dynamics. Rooted in the mean-field assumption, CMs oversimplify by assuming that every individual has the potential to “infect” any other, neglecting the inherent complexity of underlying network structures. Since social interactions follow a networked pattern with specific links between individuals based on social behaviors, joining classic CMs and network

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Ginting, Riah Ukur, Muhammad Zarlis, Poltak Sihombing, and Syahril Efendi. "Determining epidemiological patterns in disease identification using mathematical models on machine learning based multilayer structures." Eastern-European Journal of Enterprise Technologies 4, no. 4 (130) (2024): 46–53. http://dx.doi.org/10.15587/1729-4061.2024.310522.

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The object of the study is epidemiological grouping using the SEIR mathematical model on a machine learning-based multilayer network. The problems in this research are related to managing epidemiological data on a large scale to determine disease patterns and identification such as the number of recovered cases, number of infected cases and number of deaths and demographic factors. In the process, traditional methods make it difficult to carry out processes such as determining patterns and identifying diseases. So, it is necessary to use machine learning and the SEIR (Susceptible-Exposed-Infec

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Savchuk, V. S. "APPROACH TO VERIFICATION OF THE TARGET AUDIENCE MODEL OF PSYCHOLOGICAL INFLUENCE IN SOCIAL NETWORKS." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 19 (January 15, 2021): 16–23. http://dx.doi.org/10.46972/2076-1546.2020.19.02.

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Modern planning of psychological operations is not possible without the use of information technology, such as modeling tools that ensure the accuracy of planned operations and predict their results. Modern technologies also help to facilitate the perception of information through visualization. Any information, especially on social networks, is easier for analysts to process if it is presented in a schematic form. For example, the target audience of psychological influence in social networks can be represented by a graph. The article solves the urgent problem of verifying the model of the tar

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Jiang, Ping, and Xiangbin Yan. "Stability analysis and control models for rumor spreading in online social networks." International Journal of Modern Physics C 28, no. 05 (2017): 1750061. http://dx.doi.org/10.1142/s0129183117500619.

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This paper establishes a novel Susceptible-Infected-Removed (SIR) rumor spreading model for online social networks (OSNs). The model utilizes the node degree to describe the dynamic changes of the number of rumor spreaders and it can be regarded as an extension of the traditional SIR model. Stability analysis of the model reveals that the spreader in social networks has a basic reproduction number. If the basic reproduction number is less than 1, then rumors will disappear. Otherwise, rumors will persist. According to this result, we can predict the trend of rumor spreading. Then we propose an

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Liu, Yanchao, Pengzhou Zhang, Lei Shi, and Junpeng Gong. "A Survey of Information Dissemination Model, Datasets, and Insight." Mathematics 11, no. 17 (2023): 3707. http://dx.doi.org/10.3390/math11173707.

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Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the l

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Akram, Muhammad, Anam Luqman, and Ahmad N. Al-Kenani. "Certain models of granular computing based on rough fuzzy approximations." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 2797–816. http://dx.doi.org/10.3233/jifs-191165.

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An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacen

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Bertozzi, Andrea L., Shane D. Johnson, and Michael J. Ward. "Mathematical modelling of crime and security: Special Issue of EJAM." European Journal of Applied Mathematics 27, no. 3 (2016): 311–16. http://dx.doi.org/10.1017/s0956792516000176.

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This special issue of the European journal of applied mathematics features research articles that involve the application of mathematical methodologies to the modelling of a broad range of problems related to crime and security. Some specific topics in this issue include recent developments in mathematical models of residential burglary, a dynamical model for the spatial spread of riots initiated by some triggering event, the analysis and development of game-theoretic models of crime and conflict, the study of statistically based models of insurgent activity and terrorism using real-world data

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Chou, Chung-Kuang, and Ming-Syan Chen. "Learning Multiple Factors-Aware Diffusion Models in Social Networks." IEEE Transactions on Knowledge and Data Engineering 30, no. 7 (2018): 1268–81. http://dx.doi.org/10.1109/tkde.2017.2786209.

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Volchenkov, Dimitri, and Vakhtang Putkaradze. "Mathematical Theory of Social Conformity I: Belief Dynamics, Propaganda Limits, and Learning Times in Networked Societies." Mathematics 13, no. 10 (2025): 1625. https://doi.org/10.3390/math13101625.

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This paper develops a novel probabilistic theory of belief formation in social networks, departing from classical opinion dynamics models in both interpretation and structure. Rather than treating agent states as abstract scalar opinions, we model them as belief-adoption probabilities with clear decision-theoretic meaning. Our approach replaces iterative update rules with a fixed-point formulation that reflects rapid local convergence within social neighborhoods, followed by slower global diffusion. We derive a matrix logistic equation describing uncorrelated belief propagation and analyze its

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Yousef, Ali. "A fractional-order model of COVID-19 with a strong Allee effect considering the fear effect spread by social networks to the community and the existence of the silent spreaders during the pandemic stage." AIMS Mathematics 7, no. 6 (2022): 10052–78. http://dx.doi.org/10.3934/math.2022560.

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<abstract> <p>End of 2019, the world has experienced a virus known as COVID-19, which almost changed everything in our daily and social lives. Every day, experts in medicine, economics, finance, and many different fields inform the community through the media or social networks about the virus, the effects, and changes in our "new life". The virus is highly transmittable and shows different mutated forms. Therefore, to describe this attractive event, many mathematical models and studies have been applied to work on the infections and transmission risks of COVID-19. However, another

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CHMIEL, ANNA, and JANUSZ A. HOŁYST. "FLOW OF EMOTIONAL MESSAGES IN ARTIFICIAL SOCIAL NETWORKS." International Journal of Modern Physics C 21, no. 05 (2010): 593–602. http://dx.doi.org/10.1142/s012918311001535x.

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Models of message flows in an artificial group of users communicating via the Internet are introduced and investigated using numerical simulations. We assumed that messages possess an emotional character with a positive valence and that the willingness to send the next affective message to a given person increases with the number of messages received from this person. As a result, the weights of links between group members evolve over time. Memory effects are introduced, taking into account that the preferential selection of message receivers depends on the communication intensity during the r

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Volchenkov, Dimitri. "Mathematical Frameworks for Network Dynamics: A Six-Pillar Survey for Analysis, Control, and Inference." Mathematics 13, no. 13 (2025): 2116. https://doi.org/10.3390/math13132116.

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The study of dynamical processes on complex networks constitutes a foundational domain bridging applied mathematics, statistical physics, systems theory, and data science. Temporal evolution, not static topology, determines the controllability, stability, and inference limits of real-world systems, from epidemics and neural circuits to power grids and social media. However, the methodological landscape remains fragmented, with distinct communities advancing separate formalisms for spreading, control, inference, and design. This review presents a unifying six-pillar framework for the analysis o

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Gelenbe, Erol, and Omer H. Abdelrahman. "An Energy Packet Network model for mobile networks with energy harvesting." Nonlinear Theory and Its Applications, IEICE 9, no. 3 (2022): 1–15. https://doi.org/10.1587/nolta.9.1.

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Mobile communications are a powerful contributor to social and economic develop- ment worldwide, including in less developed or remote parts of the world. However they are large users of electricity through their base stations, backhaul networks and Cloud servers, so that they have a large environmental impact when they use the electric grid. On the other hand, they could operate with renewable energy sources and thus reduce their CO2 impact and be accessible even in areas where the electric grid is unavailable or unreliable. The counterpart is that intermittent sources of energy, such as phot

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Luqman, Anam, Muhammad Akram, and Florentin Smarandache. "Complex Neutrosophic Hypergraphs: New Social Network Models." Algorithms 12, no. 11 (2019): 234. http://dx.doi.org/10.3390/a12110234.

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A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their

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