A model for simulating spreading processes based on social interactions in complex networks: case studies on online social networks and epidemics

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2023.Esta tese foi motivada por dois problemas relacionados aos processos de espalhamento: a desinformação sendo disseminada em Redes Sociais Online (OSNs) e as doenças infecciosas sendo transmitidas em uma população suscetível. O principal objetivo do presente trabalho é desenvolver um modelo para simular processos de espalhamento baseado em interações sociais e que permita abordar tanto a estrutura complexa das redes, como os comportamentos evolutivos dos agentes em um nível microscópico. Para tanto, é fornecido um modelo genérico para interações sociais assíncronas entre agentes, o qual é estendido tanto para a troca de informações como para os casos de transmissão de doenças. Um modelo de evolução da infecção é construído, dispondo de transições probabilísticas entre os estágios, permitindo o uso de dados do mundo real de forma direta. Esse modelo de troca de informações pode tratar conjuntos finitos de informações, diferentemente dos modelos da literatura de dinâmica de opinião. Também, neste último estudo de caso, outros fatores que impactam a disseminação são considerados, como a autoestima da pessoa e a confiabilidade da informação. Ambos os fatores são modelados como uma extensão do modelo anterior, porém, admitindo matrizes dinâmicas para as probabilidades das interações. Métricas para capturar informações relevantes sobre a estrutura de redes complexas grandes são estudadas, concluindo que a centralidade de autovetor está intimamente relacionada à velocidade de propagação e à probabilidade de uma determinada informação prevalecer sobre as demais no modelo proposto. Estruturas de rede estáticas e dinâmicas são construídas para representar cenários relevantes em ambos os estudos de caso, as quais são baseadas em modelos de redes complexas encontrados na literatura. Além disso, um algoritmo de clusterização é modificado para identificar comunidades em redes sociais. Este algoritmo alterado é capaz de evitar erros que foram identificados na utilização de algoritmos da literatura. Por fim, conhecer como se dá a divisão em comunidades de uma rede social, e alguma métrica de centralidade sobre os agentes, permite que políticas para controlar a disseminação de informações nas OSNs sejam propostas.Abstract: This thesis is motivated by two problems related to spreading processes: misinformation being disseminated in Online Social Networks (OSNs), and infectious diseases being transmitted in a susceptible population. The main objective of the present work is developing a model for simulating spreading processes based on social interactions, which address both the networks' complex structure and the evolving behaviors of the agents on a microscopic level. To this purpose, we provide a generic model for asynchronous social interactions between agents, which is extended for both the information exchange and the disease transmission cases. We build a model for infection evolution with probabilistic transitions between stages, allowing the usage of real-world data in a straightforward way. Our information exchange model can handle finite sets of information, differently from the models in the opinion dynamics literature. Also, in this last case study, we considered other factors that impact the spreading, like the person's self-esteem and the information's reliability. We model both factors as an extension from the previous one, by admitting dynamic matrices for the interactions' probabilities. We study metrics for capturing relevant information on the structure of large and complex networks, concluding that eigenvector centrality is intimately related to the spreading speed and the probability of a given information prevailing over the others in our model. We construct static and dynamic network structures representing meaningful scenarios for both case studies, which rely on complex network models from the literature. Further, we present the modification for a clustering algorithm to identify communities in social networks. This amended algorithm can avoid errors in the clustering that we have identified while using algorithms from the literature. Finally, we show that knowing how a social network is divided into communities, and some centrality metrics about the agents, enable us for proposing policies to control the spreading of information in OSNs

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Summer 2007 Research Symposium Abstract Book

Summer 2007 volume of abstracts for science research projects conducted by Trinity College students

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

2014 GREAT Day Program

SUNY Geneseo’s Eighth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1008/thumbnail.jp