Relevant bibliographies by topics / Social networks – Mathematical models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Social networks – Mathematical models'

Author: Grafiati
Published: 10 March 2023
Last updated: 31 July 2025

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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 (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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Dissertations / Theses on the topic "Social networks – Mathematical models"

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Tang, Hon Cheong 1980. "Gravity-based trust model for web-based social networks." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112366.

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Web-based social networks have become one of the most popular applications on the Internet in recent years. However, most of the social networks rely on some simplistic trust models to manage trust information of the users, which can cause problems ranging from unsatisfied user experience to exposure to malicious users. This thesis proposes a gravity-based trust model to enhance the aggregation of personal trust information into a subjective reputation system. This new model maps all users on the social network into n-dimensional Euclidean spaces based on their direct trust information, and cr
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Corley, Courtney David. "Social Network Simulation and Mining Social Media to Advance Epidemiology." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc11053/.

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Traditional Public Health decision-support can benefit from the Web and social media revolution. This dissertation presents approaches to mining social media benefiting public health epidemiology. Through discovery and analysis of trends in Influenza related blogs, a correlation to Centers for Disease Control and Prevention (CDC) influenza-like-illness patient reporting at sentinel health-care providers is verified. A second approach considers personal beliefs of vaccination in social media. A vaccine for human papillomavirus (HPV) was approved by the Food and Drug Administration in May 2006.
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Sharabati, Walid. "Multi-mode and evolutionary networks." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3384.

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Thesis (Ph.D.)--George Mason University, 2008.<br>Vita: p. 214-215. Thesis director: Edward J. Wegman, Yasmin H. Said Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed Mar. 9, 2009). Includes bibliographical references (p. 209-213). Also issued in print.
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Bao, Qing. "Inferring diffusion models with structural and behavioral dependency in social networks." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/305.

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Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still
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Räisänen, Janne. "Random graphs as model of Peer-to-Peer social networks." Thesis, Uppsala universitet, Matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-176609.

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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.

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Botha, Leendert W. "Modeling online social networks using Quasi-clique communities." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/17859.

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Thesis (MSc)--Stellenbosch University, 2011<br>ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our appro
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Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.

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Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating differen
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Danchev, Valentin. "Spatial network structures of world migration : heterogeneity of global and local connectivity." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:81704dfc-4221-4ef4-81cf-35d89dfc364a.

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The landscape of world migration involves multiple interacting movements of people at various geographic scales, posing significant challenges to the dyadic-independence assumption underlying standard migration models. To account for emerging patterns of multilateral migration relationships, we represent world migration as a time-evolving, spatial network. The nodes in the World Migration Network (WMN) are countries located in geographic space, and the edges represent migratory movements for each decade from 1960-2000. In the first part of the thesis, we characterise the spatial network struct
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Morales, Matamoros Javier. "On-line norm synthesis for open Multi-Agent systems." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396133.

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Multi Agent Systems (MAS) are computerised systems composed of autonomous software agents that interact to solve complex problems. Within a MAS, agents require some mechanism to coordinate their activities. In the MAS literature, norms have been widely used to coordinate agents’ activities. Thus, given a MAS, a major research challenge is how to synthesise a normative system, namely a collection of norms, which supports its agents’ coordination. This dissertation focuses on the automated synthesis of norms for open Multi- Agent Systems. In an open MAS, the agent population may change along
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Books on the topic "Social networks – Mathematical models"

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Pattison, Philippa. Algebraic models for social networks. Cambridge University Press, 1993.

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Dutta, Bhaskar. Networks and groups: Models of strategic formation. Springer, 2003.

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Kesidis, George. An introduction to models of online peer-to-peer social networking. Morgan & Claypool, 2011.

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Sääskilahti, Pekka. Essays on the economics of networks and social relations. Helsinki School of Economics, 2005.

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Garson, G. David. Neural networks: An introductory guide for social scientists. Sage, 1998.

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Capecchi, Vittorio. Applications of mathematics in models, artificial neural networks and arts: Mathematics and society. Springer, 2010.

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Boyd, John Paul. Social semigroups: A unified theory of scaling and blockmodelling as applied to social networks. George Mason University Press, 1991.

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Mobius, Markus. Trust and social collateral. National Bureau of Economic Research, 2007.

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Workshop on Dynamic Social Network Modeling and Analysis (2002 Washington, D.C.). Dynamic Social Network Modeling and Analysis: Workshop summary and papers. National Academies Press, 2003.

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Leenders, Roger Th A. J. Structure and influence: Statistical models for the dynamics of actor attributes, network structure, and their interdependence. Thesis Publishers, 1995.

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Book chapters on the topic "Social networks – Mathematical models"

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Wang, Haiyan, Feng Wang, and Kuai Xu. "Ordinary Differential Equation Models on Social Networks." In Surveys and Tutorials in the Applied Mathematical Sciences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38852-2_2.

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Fedyanin, Denis. "Epistemic Mathematical Models for Analyzing Meta-opinions on Social Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93536-7_17.

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Treur, Jan. "Mathematical Details of Specific Difference and Differential Equations and Mathematical Analysis of Emerging Network Behaviour." In Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31445-3_15.

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De Sanctis, Luca, and Stefano Ghirlanda. "Shared Culture Needs Large Social Networks." In Applications of Mathematics in Models, Artificial Neural Networks and Arts. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8581-8_5.

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Sarti, Simone, and Marco Terraneo. "An Application of the Multilevel Regression Technique to Validate a Social Stratification Scale." In Applications of Mathematics in Models, Artificial Neural Networks and Arts. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8581-8_8.

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Bomba, Andriy, Natalija Kunanets, Volodymyr Pasichnyk, and Yuriy Turbal. "Mathematical and Computer Models of Message Distribution in Social Networks Based on the Space Modification of Fermi-Pasta-Ulam Approach." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97885-7_26.

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Aggrawal, Niyati, and Adarsh Anand. "Network Models." In Social Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-3.

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Serovajsky, Simon. "Mathematical models in social sciences." In Mathematical Modelling. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003035602-9.

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Duggan, Jim. "Diffusion Models." In Lecture Notes in Social Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34043-2_5.

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Bersano-Méndez, Nicolás Ignacio, Satu Elisa Schaeffer, and Javier Bustos-Jiménez. "Metrics and Models for Social Networks." In Computational Social Networks. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_5.

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Conference papers on the topic "Social networks – Mathematical models"

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Osipyan, Valeriy, E. T. J. Algharib, Arseniy Zhuck, and Kirill Litvinov. "Mathematical Models of Security Information Systems Based on the Generalized Multiplicative Knapsacks." In 2024 17th International Conference on Security of Information and Networks (SIN). IEEE, 2024. https://doi.org/10.1109/sin63213.2024.10871365.

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Zheng, Zhijing. "Study on the Stability of Nonlinear Mathematical Models in Dynamic Evolution of Complex Networks." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729509.

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Guo, Lei, Min Sheng, Junyu Liu, and Jiandong Li. "Exploring the Throughput Capacity Limits of LEO Satellite Constellation Networks: Mathematical Models and Analysis." In 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2024. https://doi.org/10.1109/wcsp62071.2024.10826930.

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Uppalaiah, B., Anjum Ara Ahmad, A. Annie Lotus, Abdul Hameed Al-Shammari, Sharmeen Izzat Hassan, and Dhaaraani R. "Recurrent Neural Networks for Efficient Solutions to Time-Series Forecasting in Complex Mathematical Models." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009479.

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Balagura, Kyrill, Helen Kazakova, Daliant Maximus, and Victoria Turygina. "Mathematical models of cognitive interaction identification in the social networks." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114453.

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Simsek, Mustafa, Ibrahim Delibalta, Lemi Baruh, and Suleyman S. Kozat. "Mathematical model of causal inference in Social Networks." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495952.

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Leung, Carson K., and Sehaj P. Singh. "A mathematical model for friend discovery from dynamic social graphs." In ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining. ACM, 2021. http://dx.doi.org/10.1145/3487351.3489473.

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Stojanov, Z., J. Stojanov, G. Jotanovic, and D. Dobrilovic. "Weighted networks in socio-technical systems: Concepts and challenges." In The International Workshop on Information, Computation, and Control Systems for Distributed Environments. Crossref, 2020. http://dx.doi.org/10.47350/iccs-de.2020.24.

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Socio-technical systems join together humans and technique. Basic concepts and principles of socio-technical systems are outlined, as well as weighted networks as the appropriate mathematical models. Particular examples of socio-technical systems with various usages of weighted networks in domains such as airline connection networks, scientific collaboration, social networks, software engineering and urban traffic are presented. Comparative analysis of the selected examples is outlined with the focus on the nature and functions of nodes, links and weights. The identified challenges, such as th
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LIÑÁN Ruiz, Roberto José, Jorge Pérez Aracil, and Víctor Cabrera Cañizares. "Mathematical optimization for planning and design of cycle paths." In CIT2016. Congreso de Ingeniería del Transporte. Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.4089.

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The daily need for citizens to move for different activities, whatever its nature, has been greatly affected by the changes. The advantages resulting from the inclusion of the bicycle as a mode of transport and the proliferation of its use among citizens are numerous and extend both in the field of urban mobility and sustainable development.Currently, there are a number of programs for the implementation, promotion or increased public participation related to cycling in cities. But ultimately, each and every one of these initiatives have the same goal, to create a mesh of effective, useful and
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Zhukov, Dmitry, and Julia Perova. "A Model for Analyzing User Moods of Self-organizing Social Network Structures Based on Graph Theory and the Use of Neural Networks." In 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2021. http://dx.doi.org/10.1109/summa53307.2021.9632203.

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Reports on the topic "Social networks – Mathematical models"

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Gelenbe, Erol. Mathematical Models by Quality of Service Driven Routing in Networks. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada436700.

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Gelenbe, Erol. Mathematical Models for Quality of Service Driven Routing in Networks. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada441501.

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Saito, Kazumi. Dynamic Trust Models between Users over Social Networks. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ada636879.

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Tarakanov, Alexander O. Development of Mathematical Models of Immune Networks Intended for Information Security Assurance. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada454473.

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Tucker-Blackmon, Angelicque. Engagement in Engineering Pathways “E-PATH” An Initiative to Retain Non-Traditional Students in Engineering Year Three Summative External Evaluation Report. Innovative Learning Center, LLC, 2020. http://dx.doi.org/10.52012/tyob9090.

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The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden
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Chandrasekhar, Arun, Horacio Larreguy, and Juan Pablo Xandri. Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21468.

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Snijders, Tom. Longitudinal Social Network Analysis. Instats Inc., 2024. https://doi.org/10.61700/zimra7qjrsnu11850.

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This three-day workshop provides an introductory overview of longitudinal social network analysis using the RSiena package, focusing on the application of Stochastic Actor-oriented Models to study network dynamics and the co-evolution of networks and behavior. Participants will gain practical skills in modeling network panel data, as well as modeling co-evolution of networks and behavior, for application of these techniques across various academic disciplines.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditio
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Salazar, Alessandro Toledo, Matthew Medrano, Mathias Duque Medina, Julio Roa, and Jorge E. Pesantez. Enhancing Evacuation Warning Responsiveness: Exploring the Impact of Social Interactions through an Agent-Based Model Approach. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2356.

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Evacuations are the preferred response to human- or natural-caused disasters. The process often involves people deciding when and how to evacuate based on messages from local authorities. However, diverse opinions of the affected people may influence their decision to evacuate or to stay and see how the situation unfolds. This project applies an opinion dynamics concept to model the opinion and decision-making of people threatened by wildfire. To demonstrate how individual opinions evolve with time, the model applies an agent-based approach that includes the interaction between an agency sendi
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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet ad
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