Relevant bibliographies by topics / Social networks – Mathematical models

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

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Published: 10 March 2023

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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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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 creates a trust social neighborhood for each user. The reputation of a target user is determined by applying gravity model to the information from both target's and observer's trust social neighborhood. A prototype of this trust model is implemented in order to evaluate the effects of varying different parameters of the gravity-based trust model.
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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. The virus is present in nearly all cervical cancers and implicated in many throat and oral cancers. Results from automatic sentiment classification of HPV vaccination beliefs are presented which will enable more accurate prediction of the vaccine's population-level impact. Two epidemic models are introduced that embody the intimate social networks related to HPV transmission. Ultimately, aggregating these methodologies with epidemic and social network modeling facilitate effective development of strategies for targeted interventions.
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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.
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 lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patterns of parent nodes form the latent classes for each node. Both the co-activation patterns and the corresponding pattern-based diffusion probabilities are inferred using a two-level EM algorithm. As compared to the component-based diffusion model, the inferred co-activation patterns can be interpreted as the soft parent components, providing insights on how each node is influenced by its neighbors as reflected by the observed cascade data. With the motivation to discover a common set of the over-represented temporal activation patterns (motifs) characterizing the overall diffusion in a social network, we further propose a motif-based diffusion model. By considering the temporal ordering of the parent activations and the social roles estimated for each node, each temporal activation motif is represented using a Markov chain with the social roles being its states. Again, a two-level EM algorithm is proposed to infer both the temporal activation motifs and the corresponding diffusion network simultaneously. The inferred activation motifs can be interpreted as the underlying diffusion mechanisms characterizing the diffusion happening in the social network. Extensive experiments have been carried out to evaluate the performance of all the proposed diffusion models using both synthetic and real data. The results obtained and presented in the thesis demonstrate the effectiveness of the proposed models. In addition, we discuss in detail how to interpret the inferred co-activation patterns and interaction motifs as the diffusion mechanisms under the context of different real social network data sets.
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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
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 approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously.
AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.
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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 different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
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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 structure of the WMN, with a particular focus on detecting and mapping mesoscopic structures called 'communities' (i.e., sets of countries with denser migration connections internally than to the rest of the WMN). We employ a method for community detection that simultaneously accounts for multilateral migration, spatial constraints, time-dependence, and directionality in the WMN. We then introduce an approach for characterising local (intracommunity) and global (intercommunity) connectivity in the WMN. On this basis, we define a threefold typology that distinguishes 'cave', 'bi-regional', and 'bridging' communities. These are characterised with distinct migration patterns, spatial network structures, and temporal dynamics: cave communities are tightly-knit enduring structures that channel local migration between contiguous countries; bi-regional communities merge migration between two distinct geographic regions; bridging communities have hub-and-spoke dynamic structures that emerge from globe-spanning movements. Our results suggest that the WMN is neither a globally interconnected network nor reproducing geographic boundaries but involves heterogeneous patterns of global and local ('glocal') migration connectivity. We examine a set of relational, homophily, and spatial mechanisms that could have possibly generated the 'glocal' structure we observe. We found that communities of different types arise from significantly different mechanisms. Our results suggest that migration communities can have important implications for world migration, as different types of community structure provide distinct opportunities and constraints, thereby distinctively shaping future migration patterns.
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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 time, agents may be developed by third parties and their behaviours are not known beforehand. These particular conditions make specially challenging to synthesise a normative system to govern an open MAS. The MAS literature has mainly investigated two general approaches to norm synthesis: off-line design, and on-line synthesis. The first approach aims at synthesising a normative system at design time. With this aim, it assumes that the MAS state space is known at design time and does not change at runtime. This goes against the nature of open MAS, and thus off-line design is not appropriate to synthesise their norms. Alternatively, on-line norm synthesis considers that norms are synthesised at runtime. Most on-line synthesis research has focused on norm emergence, which considers that agents synthesise their own norms, thus assuming that they have norm synthesis capabilities. Again, this cannot be assumed in open MAS. Against this background, this dissertation introduces a whole computational framework to perform on-line norm synthesis for open Multi-Agent Systems. Firstly, this framework provides a computational model to synthesise norms for a MAS at runtime. Such computational model requires neither knowledge about agents’ behaviours beforehand nor their participation in the norm synthesis pro- cess. Instead, it considers a regulatory entity that observes agents’ interactions at runtime, identifying situations that are undesirable for coordination to sub- sequently synthesise norms that regulate these situations. Our computational model has been conceived to be of general purpose so that it can be employed to synthesise norms in a wide range of application domains by providing little domain-dependent information. Secondly, our framework provides an abstract architecture to implement such regulatory entity (the so-called Norm Synthesis Machine), which observes a MAS and executes a synthesis strategy to synthe- sise norms. Thirdly, our framework encompasses a family of norm synthesis strategies intended to be executed by the Norm Synthesis Machine. Overall, this family of strategies supports multi-objective on-line norm synthesis Our first synthesis strategy, the so-called base, aims at synthesising effective normative systems that successfully avoid situations that are undesirable for a MAS’ coordination. Then, two further strategies (called iron and simon) go beyond effectiveness and also consider compactness as a norm synthesis goal. iron and simon take alternative approaches to synthesise compact normative systems that, in addition to effectively achieve coordination, are as synthetic as possible. This allows them to reduce agents’ computational efforts when reasoning about norms. A fourth strategy, the so-called lion, goes beyond effectiveness and compactness to also consider liberality as a synthesis goal. lion aims at synthesising normative systems that are effective and compact while preserving agents’ freedom to the greatest possible extent. Our final strategy is desmon, which is capable of synthesising norms by considering different degrees of reactivity. desmon allows to adjust the amount of information that is required to decide whether a norm must be included in a normative system or not. Thus, desmon can synthesise norms either by being reactive (i.e., by considering little information), or by being more deliberative (by employing more information). We provide empirical evaluations of our norm synthesis strategies in two application domains: a road traffic domain, and an on-line community domain. In this former domain, we employ these strategies to synthesise effective, compact and liberal normative systems that successfully avoid collisions between cars. In the latter domain, our strategies synthesise normative systems based on users’ complaints about inappropriate contents. In this way, our strategies implement a regulatory approach that synthesises norms when there is enough user consensus about the need for norms. Overall, this thesis advances in the state of the art in norm synthesis by providing a novel computational model, an abstract architecture and a family of strategies for on-line norm synthesis for open Multi-Agent Systems.
Els sistemes Multi-Agent (MAS) són sistemes computeritzats composats d’agents autònoms que interaccionen per resoldre problemes complexos. A un MAS, els agents requereixen algun mecanisme per a coordinar les seves activitats. A la literatura en Sistemes Multi-Agent, les normes han estat àmpliament utilitzades per coordinar les activitats dels agents. Per tant, donat un MAS, un dels majors reptes d’investigació és el de sintetizar el sistema normatiu, és a dir, la col·lecció de normes, que suporti la coordinació dels agents. Aquesta tesi es centra en la síntesi automàtica de normes per sistemes Multi-Agent oberts. A un MAS obert, la població d’agents pot canviar amb el temps, els agents poden ésser desenvolupats per terceres parts, i els comportaments dels agents són desconeguts per endavant. Aquestes condicions particulars fan especialment complicat sintetizar el sistema normatiu que reguli un sistema Multi-Agent obert. En general, la literatura en Sistemes Multi-Agent ha investigat dues aproximacions a la síntesi de normes: disseny off-line, i síntesi on-line. La primera aproximació consisteix a sintetizar un sistema normatiu en temps de disseny. Amb aquest propòsit, aquesta aproximació assumeix que l’espai d’estats d’un MAS és conegut en temps de disseny i no canvia en temps d’execució. Això va contra la natura dels sistemes Multi-Agent oberts, i per tant el disseny off-line no és apropiat per a sintetitzar les seves normes. Com a alternativa, la síntesi on-line considera que les normes són sintetizades en temps d’execució. La majoria de recerca en síntesi on-line s’ha centrat en la emergència de normes, que considera que els agents sintetizen les seves pròpies normes, per tant assumint que tenen la capacitat de sintetitzar-les. Aquestes condicions tampoc no es poden assumir en un MAS obert. Donat això, aquesta tesi introdueix un marc computacional per la síntesi on-line de normes en sistemes Multi-Agent oberts. Primer, aquest marc proveeix un model computacional per sintetizar normes per un MAS en temps d’execució. Aquest model computacional no requereix ni coneixement sobre els comportaments dels agents per endavant ni la seva participación en la síntesi de normes. En canvi, considera que una entitat reguladora observa les interaccions dels agents en temps d’execució, identificant situacions indesitjades per la coordinació i sintetizant normes que regulen aquestes situacions. El nostre model computacional ha estat dissenyat per a ésser de propòsit general per tal que pugui ser utilitzat a la síntesi de normes en un ampli ventall de dominis d’aplicació proporcionant només información clau sobre el domini. Segon, el nostre marc proveeix una arquitectura abstracta per implementar aquesta entitat reguladora, anomenada Màquina de Síntesi, que observa un MAS en temps d’execució i executa una estratègia de síntesi que s’encarrega de sintetizar normes. Tercer, el nostre marc incorpora una familia d’estratègies de síntesi destinades a ésser executades per una màquina de síntesi. En general, aquesta familia d’estratègies soporta la síntesi multi-objectiu i on-line de normes. La nostra primera estratègia, anomenada BASE, està dissenyada per sintetitzar sistemes normatius eficaços que evitin de manera satisfactòria situacions indesitjades per la coordinació d’un sistema Multi-Agent. Després, dues estratègies de síntesi, anomenades IRON i SIMON, van més enllà de la eficàcia i també consideren la compacitat com a objectiu de síntesi. IRON i SIMON prenen aproximacions alternatives a la síntesi de sistemes normatius compactes que, a més d’aconseguir la coordinació de manera efectiva, siguin tant sintètics com fos possible. Això permet a aquestes estratègies reduir els esforços computacionals dels agents a l’hora de raonar sobre les normes. Una quarta estratègia, anomenada LION, va més enllà de la eficàcia i la compacitat per considerar també la liberalitat com a objectiu de síntesi. Lion sintetitza sistemes normatius que són eficaços i compactes mentre preserven la llibertat dels agents tant com sigui possible. La nostra última estratègia és desmon, que és capaç de sintetizar normes considerant diferents graus de reactivitat. desmon permet ajustar la quantitat d’informació necessària per decidir si una norma cal que sigui o no inclosa a un sistema normatiu. DESMON pot sintetizar normes essent reactiu (considerant poca informació), o essent més deliberatiu (considerant més informació). En aquesta tesi presentem avaluacions empíriques de les nostres estratègies de síntesi en dos dominis d’aplicació: el domini del tràfic, i el domini de les comunitats on-line. En aquest primer domini, utilitzem les nostres estratègies per a sintetizar sistemes normatius eficaços, compactes i liberals que eviten colisions entre cotxes. Al segon domini, les nostres estratègies sintetizen sistemes normatius basant-se en les queixes dels usuaris de la comunitat sobre continguts inapropiats. D’aquesta manera, les nostres estratègies implementen un mecanisme de regulació que sintetiza normes quan hi ha suficient consens entre els usuaris sobre la necessitat de normes. Aquesta tesi avança en l’estat de l’art en síntesi de normes al proporcionar un novedós model computacional, una arquitectura abstracta i una familia d’estratègies per la síntesi on-line de normes per sistemes Multi-Agent oberts.
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Books on the topic "Social networks – Mathematical models"

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Pattison, Philippa. Algebraic models for social networks. Cambridge [England]: Cambridge University Press, 1993.

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1946-, Carrington Peter J., Scott John, and Wasserman Stanley, eds. Models and methods in social network analysis. Cambridge: Cambridge University Press, 2005.

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

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Kesidis, George. An introduction to models of online peer-to-peer social networking. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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

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Exponential random graph models for social networks: Theories, methods, and applications. Cambridge: Cambridge University Press, 2012.

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

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

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

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Un mondo piccolo ma lento: Dalla fisica ai social network. Ariccia (RM): Aracne editrice int.le S.r.l., 2015.

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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, 3–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38852-2_2.

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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, 375–403. Cham: 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, 113–22. Dordrecht: 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, 147–61. Dordrecht: 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, 257–66. Cham: 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, 37–52. Boca Raton: 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, 149–64. Boca Raton: 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, 97–121. Cham: 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, 115–42. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_5.

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Zheleva, Elena, Evimaria Terzi, and Lise Getoor. "Models of Information Sharing." In Privacy in Social Networks, 56–61. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01901-2_7.

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

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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. New York, NY, USA: 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 the creation of networks, evolving nature of systems, and the need for multidisciplinary teams in the system design are discussed.
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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. Valencia: 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 cycling trails that allow the use of bicycles in preferred routes with high guarantees of security, incorporating bicycle model intermodal urban transport.With the gradual implementation of bike lanes, many people have begun to use them to get around the city. But everything again needs a period of adaptation, and the reality is that the road network for these vehicles is full of obstacles to the rider. The current situation has led to the proposal that many kilometers of cycle paths needed to supply the demand of this mode of transport and, if implemented and planned are correct and sufficient.This paper presents a mathematical programming model for optimal design of a network for cyclists is presented. Specifically, the model determines a network of bicycle infrastructure, appropriate to the characteristics of a network of existing roads.As an application of the proposed model, the result of these experiments give a number of useful conclusions for planning and designing networks of cycle paths from a social perspective, applied to the case in the city of Malaga.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4089
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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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Tuarob, Suppawong, and Conrad S. Tucker. "Discovering Next Generation Product Innovations by Identifying Lead User Preferences Expressed Through Large Scale Social Media Data." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34767.

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An innovative consumer (a.k.a. a lead user) is a consumer of a product that faces needs unknown to the public. Innovative consumers play important roles in the product development process as their ideas tend to be innovatively unique and can be potentially useful for development of next generation, innovative products that better satisfy the market needs. Oftentimes, consumers portray their usage experience and opinions about products and product features through social networks such as Twitter and Facebook, making social media a viable, rich in information, and large-scale source for mining product related information. The authors of this work propose a data mining methodology to automatically identify innovative consumers from a heterogeneous pool of social media users. Specifically, a mathematical model is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discover innovative users from the ever increasing pool of social media users. A real-world case study, which identifies smartphone lead users in the pool of Twitter users, illustrates promising success of the proposed models.
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Costa, Gianni, and Riccardo Ortale. "Overlapping Communities and Roles in Networks with Node Attributes: Probabilistic Graphical Modeling, Bayesian Formulation and Variational Inference (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/796.

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We study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm. A wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services.
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Davidson, Jacob D., and N. C. Goulbourne. "Connecting Chain Chemistry and Network Topology With the Large Deformation Mechanical Response of Elastomers." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88551.

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Elastomers are polymers able to undergo large, reversible deformations, and their mechanical properties depend on the chemistry of individual chains as well as the topology of the crosslinked network. In this work we analyze the connection between micro-scale network structure and the macroscopic mechanical properties by performing molecular dynamics (MD) simulations using the Kremer & Grest bead-spring model. The chain length and the density at which crosslinking is performed are varied in order to produce systems ranging from crosslink-dominated to highly entangled, and stress-stretch results are obtained via MD in the large deformation regime. In analogy with recent work on social, technological, and biological networks, we apply mathematical graph theory to describe elastomer networks in a multi-scale modeling framework. A matrix formulation of crosslinked polymers is presented and applied in order to identify the network structure resulting from both chemical crosslinks and physical crosslinks (entanglements). We show that spectral analysis of the crosslink and chain entanglement adjacency matrices along with the corresponding degree distributions can be used to identify and differentiate between the different materials. The spectrum of the crosslink adjacency matrix resembles a sparse regular graph, and spectrum of the intermolecular chain entanglement matrix for the highly entangled systems is shown to resemble a random graph; however, deviations are noted which require further study. A comparison of the network properties with the stress-stretch response demonstrates the influence of both crosslinks and entanglements on the large deformation mechanical behavior of an elastomer material.
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Eshghi, Soheil, Grace-Rose Williams, Gualtiero B. Colombo, Liam D. Turner, David G. Rand, Roger M. Whitaker, and Leandros Tassiulas. "Mathematical models for social group behavior." In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017. http://dx.doi.org/10.1109/uic-atc.2017.8397423.

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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. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada436700.

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

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Saito, Kazumi. Dynamic Trust Models between Users over Social Networks. Fort Belvoir, VA: Defense Technical Information Center, March 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. Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada454473.

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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. Cambridge, MA: National Bureau of Economic Research, August 2015. http://dx.doi.org/10.3386/w21468.

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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, July 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 engineering participation. Knowledge generated around using peer-facilitated instruction at two-year colleges can improve underrepresented students' success and participation in engineering across a broad range of institutions. Students in the program participated in peer-facilitated recitation sessions linked to fundamental engineering courses, such as engineering analysis, statics, and dynamics. These courses have the highest failure rate among women and underrepresented minority students. As a mixed-methods evaluation study, student engagement was measured as students' comfort with asking questions, collaboration with peers, and applying mathematics concepts. SPSS was used to analyze pre-and post-surveys for statistical significance. Qualitative data were collected through classroom observations and focus group sessions with recitation leaders. Semi-structured interviews were conducted with faculty members and students to understand their experiences in the program. Findings revealed that women students had marginalization and intimidation perceptions primarily from courses with significantly more men than women. However, they shared numerous strategies that could support them towards success through the engineering pathway. Women and underrepresented students perceived that they did not have a network of peers and faculty as role models to identify within engineering disciplines. The recitation sessions had a positive social impact on Hispanic women. As opportunities to collaborate increased, Hispanic womens' social engagement was expected to increase. This social engagement level has already been predicted to increase women students' persistence and retention in engineering and result in them not leaving the engineering pathway. An analysis of quantitative survey data from students in the three engineering courses revealed a significant effect of race and ethnicity for comfort in asking questions in class, collaborating with peers outside the classroom, and applying mathematical concepts. Further examination of this effect for comfort with asking questions in class revealed that comfort asking questions was driven by one or two extreme post-test scores of Asian students. A follow-up ANOVA for this item revealed that Asian women reported feeling excluded in the classroom. However, it was difficult to determine whether these differences are stable given the small sample size for students identifying as Asian. Furthermore, gender differences were significant for comfort in communicating with professors and peers. Overall, women reported less comfort communicating with their professors than men. Results from student metrics will inform faculty professional development efforts to increase faculty support and maximize student engagement, persistence, and retention in engineering courses at community colleges. Summative results from this project could inform the national STEM community about recitation support to further improve undergraduate engineering learning and educational research.
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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.], June 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 traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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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.], November 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 add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.
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Shabelnyk, Tetiana V., Serhii V. Krivenko, Nataliia Yu Rotanova, Oksana F. Diachenko, Iryna B. Tymofieieva, and Arnold E. Kiv. Integration of chatbots into the system of professional training of Masters. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4439.

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The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.
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Klymenko, Mykola V., and Andrii M. Striuk. Development of software and hardware complex of GPS-tracking. CEUR Workshop Proceedings, March 2021. http://dx.doi.org/10.31812/123456789/4430.

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The paper considers the typical technical features of GPS-tracking systems and their development, as well as an analysis of existing solutions to the problem. Mathematical models for the operation of hardware and software of this complex have been created. An adaptive user interface has been developed that allows you to use this complex from a smartphone or personal computer. Methods for displaying the distance traveled by a moving object on an electronic map have been developed. Atmega162-16PU microcontroller software for GSM module and GPS receiver control has been developed. A method of data transfer from a GPS tracker to a web server has been developed. Two valid experimental samples of GPS-trackers were made and tested in uncertain conditions. The GPS-tracking software and hardware can be used to monitor the movement of moving objects that are within the coverage of GSM cellular networks.
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