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Journal articles on the topic 'Social networks – Mathematical models'
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Anderson, Brian D. O., and Mengbin Ye. "Mathematical Models of Self-Appraisal in Social Networks." Journal of Systems Science and Complexity 34, no. 5 (October 2021): 1604–33. http://dx.doi.org/10.1007/s11424-021-1193-y.
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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, we propose a new model of opinion propagation, where the opinion of an agent is described by a Gaussian density, and the (discrete) evolution equation is based on barycenters with respect to the Fisher metric.
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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 interpretation. After a discussion of the obtained results of model simulations, multidisciplinary work perspectives (both on mathematical and biomedical sides) are proposed in order to increase the efficiency of the models currently used and improve both the comprehension of the contagion mechanism and the prediction of the dynamic behaviors of the pandemics' present and future states.</p> </abstract>
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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.
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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.
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Assunção, Diana, Isabel Pedrosa, Rui Mendes, Fernando Martins, João Francisco, Ricardo Gomes, and Gonçalo Dias. "Social Network Analysis: Mathematical Models for Understanding Professional Football in Game Critical Moments—An Exploratory Study." Applied Sciences 12, no. 13 (June 24, 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 far as it concerned, we found that the Centroid Players had a decisive role in the level of connectivity and interaction of the team. Regarding the main critical moments of the game, the results showed that these were preceded by periods of great instability, obtaining a differentiated performance in the following mathematical models: Centrality, Degree Centrality, Closeness Centrality, and Degree Prestige. We concluded that the networks approach, in concomitance with the dynamic properties of mathematical models, and the critical moments of the game, can help coaches to better evaluate the level of interaction and connectivity of their players toward the actions imposed by opponents.
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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 (July 17, 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 results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on their digital footprints. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. Mathematical models were developed to quickly detect, prevent, and address conflicts in urban planning in order to manage urban systems efficiently. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation around the construction of the NEC, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts. Research data could be of use in solving similar problems in sociology, ecology, and economics.
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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 (December 29, 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 platform for digital marketing and sharing information. However, on the other end, social network rumors and fake news create a significant impact on society, including riots. To study and analyze social network rumors, several mathematical rumor propagation epidemic models have been proposed. The majority are related to disease-spreading epidemic models and reject the human aspect of social selection. This paper introduces a new mathematical rumor propagation model for social networks by incorporating the human psychological aspect of selection as a separate state. Our mathematical analysis and computational simulation proved that the model exists within the system. It was also proven that the system is always non-negative and there always exists a solution in the system. Our implementation of an intervention mechanism within the discrete compartmental model simulation proved the necessity of an effective interference that can help to prevent the implications of uncontrolled rumor dissemination within social networks.
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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 (January 2009): 285–313. http://dx.doi.org/10.1080/15427951.2009.10390642.
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Saunders, Clare. "Unblocking the Path to Effective Block Modeling in Social Movement Research." Mobilization: An International Quarterly 16, no. 3 (September 1, 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 of equivalence should be carefully selected. Some dilemmas involved in block modelling analysis are demonstrated through block modelling analysis of interorganizational networking in Friends of the Earth International (FoEI). This illustrates that, in the absence of the robust analytical procedures recommended by mathematical sociologists, block models are unable to uncover the structure of social movement networks.
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Tsocheva, Ksenia Ivova. "Mathematical Analysis of Some Reaction Networks Inducing Biological Growth/Decay Functions." Biomath Communications 7, no. 1 (July 17, 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 when testing various appropriate models, especially when studying growth processes in life sciences. The proposed reaction network realization of Gompertz growth model can be interpreted from the perspective of demographic and socio-economic sciences. The reaction network approach clearly explains the intimate links between the Gompertz model and the Verhulst logistic model. There are shown reversible reactions which complete the already known non-reversible ones. It is also demonstrated that the proposed approach can be applied in oscillating processes and social-science events. The paper is richly illustrated with numerical computations and computer simulations performed by algorithms using the computer algebra system Mathematica.
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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 (March 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 prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.
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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 (March 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 explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak.
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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 (January 2010): 26–28. http://dx.doi.org/10.1016/j.elerap.2009.11.007.
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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 (October 9, 2014): 435102. http://dx.doi.org/10.1088/1751-8113/47/43/435102.
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Adams, Johnathan A., Gentry White, and Robyn P. Araujo. "Mathematical measures of societal polarisation." PLOS ONE 17, no. 10 (October 4, 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 Kullback–Leibler divergence and the Hellinger distance as polarisation measures. We present a series of opinion dynamics simulations from two common models to test the effectiveness of the methods. Results show that the four measures provide insight into the different aspects of polarisation and allow real-time monitoring of social networks for indicators of polarisation. The three measures, the spectral radius, Kullback–Leibler divergence and Hellinger distance, smoothly delineated between different amounts of polarisation, i.e. how many cluster there were in the simulation, while also measuring with more granularity how close simulations were to consensus. Min-max flow failed to accomplish such nuance.
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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 cyber fraud: social engineering, phishing, stalking, farming, DoS attacks, online fraud, potentially unwanted programs, etc. The study also considered a model of cognitive computing and detection of suspicious transactions in banking cyber-physical systems based on quantum computing in BCPS for the post-quantum era. The advantages, disadvantages and results of the model are defined. Predictive modelling is proposed to detect fraud in real-time by analysing incoming bank transactions with payment cards. Within the framework of this method, such models are used for the classification of fraud detection as logistic regression, a decision tree, and a narrower technique - a random forest decision tree. The study also considered using the harmonic search algorithm in neural networks to improve fraud detection in the banking system. It is found that although this model has the advantage of learning ability based on past behaviour, there are difficulties in the long-term processing of many neural networks. The stages of model implementation are also given. In addition, the modelling of credit card fraud detection is based on using two types of models: supervised and unsupervised. Supervised models include logistic regression, K-nearest neighbours, and extreme gradient boosting. The one-class support vector model, restricted Boltzmann model, and generative-competitive network are considered among uncontrolled generative models.
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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 (October 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 form of computer programs (software) and to test them on real sociological data.
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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 (March 9, 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 immune-structure SIR model to explore the control method of rumor spreading. Stability analysis and numerical simulation of the model indicate that immunizing susceptible individual is an effective method to control rumors. Further, the immune-structure model explains that the network structure decides the choice of immune methods. Our findings offer some new insights to control the spread of rumors on OSNs.
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Akhramovych, Volodymyr, German Shuklin, Yuriy Pepa, Tetiana Muzhanova, and Serhii Zozulia. "Devising a procedure to determine the level of informational space security in social networks considering interrelations among users." Eastern-European Journal of Enterprise Technologies 1, no. 9(115) (February 28, 2022): 63–74. http://dx.doi.org/10.15587/1729-4061.2022.252135.
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+ Linear and dynamic models of the system of information security in social networks, taking into consideration the relationships between users, were studied and the resistance of the security system was analyzed. There is a practical interest in studying dependence of the behavior of the system of social network security on the parameters of users’ interaction. Dynamic systems of information security in social networks in the mathematical sense of this term were considered. A dynamic system refers to any object or process, for which the concept of state as a totality of certain magnitudes at a given time is unambiguously defined and the law that describes a change (evolution) of the initial state over time was assigned. The network of social interactions consists of a totality of social users and a totality of the relations between them. Individuals, social groups, organizations, cities, countries can act as users. Relations imply not only communication interactions between users but also relations of the exchange of various resources and activities, including conflict relations. As a result of research, it was found that the security systems of a social network are nonlinear. Theoretical study of the dynamic behavior of an actual object requires the creation of its mathematical model. The procedure for developing a model is to compile mathematical equations based on physical laws. These laws are stated in the language of differential equations. Phase portraits of the data security system in the MATLAB/Multisim program, which indicate the stability of a security system in the working range of parameters even at the maximum value of the impacts, were determined. Thus, the influence of users’ interaction parameters on the parameters of the system of social network security was explored. Such study is useful and important in terms of information security in the network, since the parameters of users’ interaction significantly affect, up to 100 %, the security indicator.
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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 (October 26, 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 paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.
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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 (April 19, 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 controlling COVID-19, even in the absence of a vaccine. With a vaccine, social distancing minimizes the sufficient vaccination rate to control the disease. Community awareness also has a great impact in eradicating the virus transmission. The model is simulated on small-world networks and the role of social distancing in controlling the infection is explained.
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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 (October 7, 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 adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.
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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 (June 19, 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 calculations. The experiment simulates a virtual social network based on a social networking site architecture from the data set used to conduct experiments on the enhanced GADM and observe their influence capabilities. Findings The presence of social trust can effectively increase the rate of successfully influenced social nodes by factorizing trust value of one source node and acceptance rate of another recipient node into its probabilistic equation, hence increasing the final acceptance probability. Research limitations/implications This research focused exclusively on conceptual mathematical models and technical aspects so far; comprehensive user study, extensive performance and scalability testing is left for future work. Originality/value Two key contributions of this paper are the calculation of social trust via content integrity and the application of social trust in social influential diffusion algorithms. Two models will be designed, implemented and evaluated on the application of social trust via trusted social nodes and domain-specified (of specific interest groups) trusted social nodes.
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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 (April 28, 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 sets, models for the optimal strategy of police deployment under realistic constraints, and a model of cyber crime as related to the study of spiking behaviour in social network cyberspace communications. Overall, the mathematical and computational methodologies employed in these studies are as diverse as the specific applications themselves and the scales (spatial or otherwise) to which they are applied. These methodologies range from statistical and stochastic methods based on maximum likelihood methods, Bayesian equilibria, regression analysis, self-excited Hawkes point processes, agent-based random walk models on networks, to more traditional applied mathematical methods such as dynamical systems and stability theory, the theory of Nash equilibria, rigorous methods in partial differential equations and travelling wave theory, and asymptotic methods that exploit disparate space and time scales.
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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 target audience of psychological influence in social networks. The approach to model verification is based on a combination of cognitive criteria of topological structure. Classical methods of verification of mathematical models are used for verification of mathematical calculations. Data visualization is taken as the basis of the cognition criterion. The article presents a method of graph visualization, which sets the mathematical model of the target audience. Visual representation of the graph not only promotes better assimilation, but also speeds up the analysis of the collected information about the target audience through automation. The data obtained during the run of the model are compared with real data, thus confirming the conformity of the behavior of the model to real phenomena. Validation of the model with the help of software was performed by tracing the model. The adequacy of the model is based on the possibility of analysis of the target audience by the user according to the characteristics of individual actors, which make it possible to determine their role and analyze the structure of the target audience as a whole. The theory of analysis of social networks is the basis of the choice of characteristics of the target audience. To assess the adequacy of the model, two characteristics of the target audience were chosen, as centrality in mediation and centrality in its own vector. The results of the calculations are presented using the software application Gephi. Data from the VKontakte social network were used as input data to check the adequacy of the model.
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Gnedash, Anna, and Veronika Katermina. "Abortion Ban in English Social Media in 2022: Pragmatic Linguistics of Online Communications." Virtual Communication and Social Networks 2022, no. 4 (December 22, 2022): 172–78. http://dx.doi.org/10.21603/2782-4799-2022-1-4-172-178.
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The present research featured the conflict discourse of online communication in social networks on the topic of abortion ban. The empirical base included 3,000,000 Twitter messages in English. The sampling by keywords women and abortion covered the period from May 1 to July 31, 2022, which saw an outburst of online and offline civic activities regarding some national anti-abortion policies. The resulting web corpus of network linguistic data (datasets) was subjected to multidimensional analysis using such methods as Data Science, mathematical modeling, relational sociology, corpus analysis, discourse analysis, etc. All these procedures combined resulted in a multidimensional comprehensive analysis of the simulated English asynchronous multimodal discursive field in Twitter. The models made it possible to visualize online communications in social networks, as well as to describe the discourse of online communication between pro-choice and pro-life. The authors analyzed the pragmatic potential of network communities on the current political agenda. The method might help to identify the conflict potential that can evolve from online communication into offline socio-political actions.
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LI, PEI, KAI XING, DAPENG WANG, XIN ZHANG, and HUI WANG. "INFORMATION DIFFUSION IN FACEBOOK-LIKE SOCIAL NETWORKS UNDER INFORMATION OVERLOAD." International Journal of Modern Physics C 24, no. 07 (June 6, 2013): 1350047. http://dx.doi.org/10.1142/s0129183113500472.
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Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.
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Lin, Wei, Li Xu, and He Fang. "Finding influential edges in multilayer networks: Perspective from multilayer diffusion model." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 10 (October 2022): 103131. http://dx.doi.org/10.1063/5.0111151.
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With the popularization of social network analysis, information diffusion models have a wide range of applications, such as viral marketing, publishing predictions, and social recommendations. The emergence of multiplex social networks has greatly enriched our daily life; meanwhile, identifying influential edges remains a significant challenge. The key problem lies that the edges of the same nodes are heterogeneous at different layers of the network. To solve this problem, we first develop a general information diffusion model based on the adjacency tensor for the multiplex network and show that the [Formula: see text]-mode singular value can control the level of information diffusion. Then, to explain the suppression of information diffusion through edge deletion, efficient edge eigenvector centrality is proposed to identify the influence of heterogeneous edges. The numerical results from synthetic networks and real-world multiplex networks show that the proposed strategy outperforms some existing edge centrality measures. We devise an experimental strategy to demonstrate that influential heterogeneous edges can be successfully identified by considering the network layer centrality, and the deletion of top edges can significantly reduce the diffusion range of information across multiplex networks.
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Edoh, Kossi, and Elijah MacCarthy. "Network and equation-based models in epidemiology." International Journal of Biomathematics 11, no. 03 (April 2018): 1850046. http://dx.doi.org/10.1142/s1793524518500468.
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Network and equation-based (EB) models are two prominent methods used in the study of epidemics. While EB models use a global approach to model aggregate population, network models focus on the behavior of individuals in the population. The two approaches have been used in several areas of research, including finance, computer science, social science and epidemiology. In this study, epidemiology is used to contrast EB models with network models. The methods are based on the assumptions and properties of compartmental models. In EB models we solve a system of ordinary differential equations and in network models we simulate the spread of epidemics on contact networks using bond percolation. We examine the impact of network structures on the spread of infection by considering various networks, including Poisson, Erdős Rényi, Scale-free, and Watts–Strogatz small-world networks, and discuss how control measures can make use of the network structures. In addition, we simulate EB assumptions on Watts–Strogatz networks to determine when the results are similar to that of EB models. As a case study, we use data from the 1918 Spanish flu pandemic and that from measles outbreak to validate our results.
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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 (July 1, 2018): 1268–81. http://dx.doi.org/10.1109/tkde.2017.2786209.
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Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.
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Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today. This article discusses the possibility of using artificial neural networks for medium-term forecasting of the power consumption of an enterprise. The task of constructing an artificial neural network using a feedback algorithm for training a network based on the Matlab mathematical package has been implemented. The authors have analyzed such characteristics as parameter setting, implementation complexity, learning rate, convergence of the result, forecasting accuracy, and stability. The results obtained led to the conclusion that the feedback algorithm is well suited for medium-term forecasting of power consumption.
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DU, FANG, QI XUAN, and TIE-JUN WU. "EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS." International Journal of Modern Physics C 21, no. 07 (July 2010): 955–71. http://dx.doi.org/10.1142/s0129183110015592.
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Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.
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Zhang, Huixin, Xueming Liu, Qi Wang, Weidong Zhang, and Jianxi Gao. "Co-adaptation enhances the resilience of mutualistic networks." Journal of The Royal Society Interface 17, no. 168 (July 2020): 20200236. http://dx.doi.org/10.1098/rsif.2020.0236.
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Mutualistic networks, which describe the ecological interactions between multiple types of species such as plants and pollinators, play a paramount role in the generation of Earth’s biodiversity. The resilience of a mutualistic network denotes its ability to retain basic functionality when errors and failures threaten the persistence of the community. Under the disturbances of mass extinctions and human-induced disasters, it is crucial to understand how mutualistic networks respond to changes, which enables the system to increase resilience and tolerate further damages. Despite recent advances in the modelling of the structure-based adaptation, we lack mathematical and computational models to describe and capture the co-adaptation between the structure and dynamics of mutualistic networks. In this paper, we incorporate dynamic features into the adaptation of structure and propose a co-adaptation model that drastically enhances the resilience of non-adaptive and structure-based adaptation models. Surprisingly, the reason for the enhancement is that the co-adaptation mechanism simultaneously increases the heterogeneity of the mutualistic network significantly without changing its connectance. Owing to the broad applications of mutualistic networks, our findings offer new ways to design mechanisms that enhance the resilience of many other systems, such as smart infrastructures and social–economical systems.
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Attri, Vikas. "Comparative study of Existing Models for Online Social Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 483–90. http://dx.doi.org/10.17762/turcomat.v12i2.856.
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Today, Online Social Networks becomes the first choice for businesses to broadcast their campaigns for branding, publicity, strategies, advertising, marketing, social influence and so many other areas. Social Network is a platform for communicating with social actors and Social Media is used by companies for broadcasting information. Online Social Network used by businesses for number of purposes but the primary concern is build new social connections that helps to target most audiences for successful campaign purposes. In OSNs sites the social objects are represented by nodes and the term edge used for connection between nodes under the graph theory. Today Social Network sites have becomes most exploded as compared to traditional sites because of impact of so many influence models over traditional models.Some of popular websites of OSN such as MySpace, Facebook, Flickr, YouTube, Google Video, Orkut, LinkedIn, Live Journal and BlogSpot have great impact on customer when targeting the sales marketing funnel for businesses. Adjacent users sometimes called engaged users tend to have more trust level as compared to random pairs users on the social media sites. Already have so much research that helps to calculate the trust factor using influence modeling. So influence models play a vital role to predict the behavior of the customer that helps to fulfill the goal of the business. The key contribution of this work is study of online social networking models.
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Pattison, Philippa, and Stanley Wasserman. "Constructing Algebraic Models for Local Social Networks Using Statistical Methods." Journal of Mathematical Psychology 39, no. 1 (March 1995): 57–72. http://dx.doi.org/10.1006/jmps.1995.1005.
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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 (May 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 recent period only. We also model the phenomenon of secondary social sharing when the reception of an emotional e-mail triggers the distribution of several emotional e-mails to other people.
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Li, Yan. "Deep Learning-Based Natural Language Processing Methods for Sentiment Analysis in Social Networks." Mathematical Problems in Engineering 2022 (July 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/1390672.
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This paper presents an in-depth study of the sentiment of social network communication through a deep learning-based natural language processing approach and designs a corresponding model to be applied in the actual social process. Specifically, the network can dynamically select the most important word in the current state according to the information available and achieve the accurate recognition of the dynamically changing important content in a sentence. Based on this, the semantic understanding of the whole sentence is achieved through a continuous cycle of the process. In addition, considering that the semantic representation of natural language is highly dependent on contextual information, the lack of contextual information will lead to the ambiguity and inaccuracy of semantic representation. In this paper, we study the sentiment analysis algorithms in social networks at two levels, unimodal and multimodal, and construct a text sentiment analysis model and a picture-text multimodal sentiment analysis model in social networks, respectively. By comparing the experiments with the existing models on several datasets, the accuracy of the two models exceeded the benchmark models by 4.45% and 5.2%, respectively, which verified the effectiveness of the two models. The feasibility of applying the optimized convolutional neural network recurrent optimization network to social network sentiment analysis is verified by practically applying the optimized convolutional neural network recurrent optimization network to single task and multitask and comparing other existing deep learning classifiers.
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Luqman, Anam, Muhammad Akram, and Florentin Smarandache. "Complex Neutrosophic Hypergraphs: New Social Network Models." Algorithms 12, no. 11 (November 6, 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 certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.
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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 (May 2, 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 application of the offered model is confirmed
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Lebiedź, Jacek, Piotr Cofta, and Cezary Orłowski. "Eventual Convergence of the Reputation-Based Algorithm in IoT Sensor Networks." Sensors 21, no. 18 (September 16, 2021): 6211. http://dx.doi.org/10.3390/s21186211.
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Uncertainty in dense heterogeneous IoT sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the purpose of IoT networks has not been widely studied, and results of simulations are often taken in lieu of the more rigorous proof. Therefore the question remains, whether under stable conditions, in realistic situations found in IoT networks, this algorithm indeed converges. This paper demonstrates proof of the eventual convergence of the EWMA algorithm. The proof consists of two steps: it models the sensor network as the UOG (Uniform Opinion Graph) that enables the analytical approach to the problem, and then offers the mathematical proof of eventual convergence, using formalizations identified in the previous step. The paper demonstrates that the EWMA algorithm converges under all realistic conditions.
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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 of personal information protection is constantly growing. To address this issue, let’s propose a method of assessing the dependence of personal data protection on the amount of information in the system and trust in social networks. The method is based on a mathematical model to determine the protection of personal data from trust in social networks. Based on the results of the proposed model, modeling was performed for different types of changes in confidence parameters and the amount of information in the system.
As a result of mathematical modeling in the MatLab environment, graphical materials were obtained, which showed that the protection of personal data increases with increasing factors of trust in information. The dependence of personal data protection on trust is proportional to other data protection parameters. The protection of personal data is growing from growing factors of trust in information.
Mathematical modeling of the proposed models of dependence of personal data protection on trust confirmed the reliability of the developed model and proved that the protection of personal data is proportional to reliability and trust
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Lefevr, Nickie, Andreas Kanavos, Vassilis C. Gerogiannis, Lazaros Iliadis, and Panagiotis Pintelas. "Employing Fuzzy Logic to Analyze the Structure of Complex Biological and Epidemic Spreading Models." Mathematics 9, no. 9 (April 27, 2021): 977. http://dx.doi.org/10.3390/math9090977.
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Complex networks constitute a new field of scientific research that is derived from the observation and analysis of real-world networks, for example, biological, computer and social ones. An important subset of complex networks is the biological, which deals with the numerical examination of connections/associations among different nodes, namely interfaces. These interfaces are evolutionary and physiological, where network epidemic models or even neural networks can be considered as representative examples. The investigation of the corresponding biological networks along with the study of human diseases has resulted in an examination of networks regarding medical supplies. This examination aims at a more profound understanding of concrete networks. Fuzzy logic is considered one of the most powerful mathematical tools for dealing with imprecision, uncertainties and partial truth. It was developed to consider partial truth values, between completely true and completely false, and aims to provide robust and low-cost solutions to real-world problems. In this manuscript, we introduce a fuzzy implementation of epidemic models regarding the Human Immunodeficiency Virus (HIV) spreading in a sample of needle drug individuals. Various fuzzy scenarios for a different number of users and different number of HIV test samples per year are analyzed in order for the samples used in the experiments to vary from case to case. To the best of our knowledge, analyzing HIV spreading with fuzzy-based simulation scenarios is a research topic that has not been particularly investigated in the literature. The simulation results of the considered scenarios demonstrate that the existence of fuzziness plays an important role in the model setup process as well as in analyzing the effects of the disease spread.
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Nguyen, Minh, Mehmet Aktas, and Esra Akbas. "Bot Detection on Social Networks Using Persistent Homology." Mathematical and Computational Applications 25, no. 3 (September 4, 2020): 58. http://dx.doi.org/10.3390/mca25030058.
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The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features.
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Tomassini, Marco, and Alberto Antonioni. "Computational Behavioral Models for Public Goods Games on Social Networks." Games 10, no. 3 (September 2, 2019): 35. http://dx.doi.org/10.3390/g10030035.
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Cooperation is a fundamental aspect of well-organized societies and public good games are a useful metaphor for modeling cooperative behavior in the presence of strong incentives to free ride. Usually, social agents interact to play a public good game through network structures. Here, we use social network structures and computational agent rules inspired by recent experimental work in order to develop models of agent behavior playing public goods games. The results of our numerical simulations based on a couple of simple models show that agents behave in a manner qualitatively similar to what has been observed experimentally. Computational models such as those presented here are very useful to interpret observed behavior and to enhance computationally the limited variation that is possible in the experimental domain. By assuming a priori reasonable individual behaviors, the easiness of running simulations could also facilitate exploration prior to any experimental work in order to vary and estimate a number of key parameters that would be very difficult, if not impossible, to change during the actual experiment.
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Mikhailova, Svetlana Viktorovna. ""Fakes" in social networking media and modeling "fake infection"." Personality & Society 2, no. 4 (October 22, 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 and analyze the system-phase pattern of the spread of fakes in the space of social interactions. "Fakes" are deliberately false, intended for manipulation. In recent years, they are easily distributed in social networks. In the work by methods of the theory of ordinary differential equations, their qualitative analysis, the above problem was full investigated. The study was conducted under assumptions: remote distributors are not allowed to participate in the transmission of fakes; an adult population susceptible to fakes has a constant birth rate; propagation can occur "vertically," wherein the transmission mechanism is introduced into the model by appropriate assumptions about the proportion of susceptible and distributors. The problem is fully investigated (solvability, unambiguity, phase patterns of stable behavior). The work will be useful in the practical identification and prediction of the influence of fake news.
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Robins, Garry, Philippa Pattison, and Stanley Wasserman. "Logit models and logistic regressions for social networks: III. Valued relations." Psychometrika 64, no. 3 (September 1999): 371–94. http://dx.doi.org/10.1007/bf02294302.
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Liu, Xiaoyang, Chao Liu, and Xiaoping Zeng. "Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling." Complexity 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/5857372.
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Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.
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Hoff, Peter D. "Multiplicative latent factor models for description and prediction of social networks." Computational and Mathematical Organization Theory 15, no. 4 (October 1, 2008): 261–72. http://dx.doi.org/10.1007/s10588-008-9040-4.
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Cifuentes-Faura, Javier, Ursula Faura-Martínez, and Matilde Lafuente-Lechuga. "Mathematical Modeling and the Use of Network Models as Epidemiological Tools." Mathematics 10, no. 18 (September 15, 2022): 3347. http://dx.doi.org/10.3390/math10183347.
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Mathematical modeling has served as an epidemiological tool to enhance the modeling efforts of the social and economic impacts of the pandemic. This article reviews epidemiological network models, which are conceived as a flexible way of representing objects and their relationships. Many studies have used these models over the years, and they have also been used to explain COVID-19. Based on the information provided by the Web of Science database, exploratory, descriptive research based on the techniques and tools of bibliometric analysis of scientific production on epidemiological network models was carried out. The epidemiological models used in the papers are diverse, highlighting those using the SIS (Susceptible-Infected-Susceptible), SIR (Susceptible-Infected-Recovered) and SEIR (Susceptible-Exposed-Infected-Removed) models. No model can perfectly predict the future, but they provide a sufficiently accurate approximation for policy makers to determine the actions needed to curb the pandemic. This review will allow any researcher or specialist in epidemiological modeling to know the evolution and development of related work on this topic.
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Chamley, Christophe, Anna Scaglione, and Lin Li. "Models for the Diffusion of Beliefs in Social Networks: An Overview." IEEE Signal Processing Magazine 30, no. 3 (May 2013): 16–29. http://dx.doi.org/10.1109/msp.2012.2234508.
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