Propagação de rumores em redes com triângulos

• Ano de defesa: 2025
• Autor(a) principal: Silva, Larissa Oliveira Moutinho da
• Orientador(a): Peron, Thomas Kauê Da'Maso
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
• Palavras-chave em Português: Redes complexas; Estrutura de triângulos; Modelo configuracional; Modelo Maki Thompson; Propagação de rumores
• Palavras-chave em Inglês: Complex networks; Triangule structure; Configurational model; Maki-Thompson model; Rumor propagation
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::ANALISE DE DADOS
• Link de acesso: https://hdl.handle.net/20.500.14289/22182

This study investigates the influence of the clustering coefficient on the dynamics of rumor propagation in complex networks, comparing it with the classical configurational model. We use the Maki-Thompson model as a basis for simulating rumor dissemination and analyze the limitations of the configurational model in reproducing the structural properties of real networks. To control the clustering coefficient without altering other structural metrics, we adopt the approach independently proposed by Newman and Miller. Through simulations, we evaluate how local clustering affects information dissemination, highlighting its impact on rumor propagation. Our results indicate that networks with a high clustering coefficient tend to restrict propagation to local communities, while networks with low clustering allow faster and more global dissemination. However, we observe that the influence of clustering on network dynamics only becomes significant when degree-degree correlations can no longer be neglected, as both properties are intrinsically related. Furthermore, we show that as the average degree increases, the influence of clustering becomes negligible, as global connectivity starts to dominate the process. By analyzing metrics such as propagator density and susceptibility, we find that clustering significantly affects the system’s response to small fluctuations, especially in networks with a low average degree. We conclude that network structure can be manipulated to either optimize or contain the spread and suggest future research to explore models that more realistically incorporate the influence of local substructures on network dynamics.