| Full text | |
| Author(s): |
Pineda, Aruane M.
;
Kent, Paul
;
Connaughton, Colm
;
Rodrigues, Francisco A.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT; v. 2023, n. 12, p. 32-pg., 2023-12-01. |
| Abstract | |
In this article, we consider machine learning algorithms to accurately predict two variables associated with the Q-voter model in complex networks, i.e. (i) the consensus time and (ii) the frequency of opinion changes. Leveraging nine topological measures of the underlying networks, we verify that the clustering coefficient (C) and information centrality emerge as the most important predictors for these outcomes. Notably, the machine learning algorithms demonstrate accuracy across three distinct initialization methods of the Q-voter model, including random selection and the involvement of high- and low-degree agents with positive opinions. By unraveling the intricate interplay between network structure and dynamics, this research sheds light on the underlying mechanisms responsible for polarization effects and other dynamic patterns in social systems. Adopting a holistic approach that comprehends the complexity of network systems, this study offers insights into the intricate dynamics associated with polarization effects and paves the way for investigating the structure and dynamics of complex systems through modern methods of machine learning. (AU) | |
| FAPESP's process: | 19/23293-0 - Prediction and inference in complex systems |
| Grantee: | Francisco Aparecido Rodrigues |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 21/13843-2 - Machine learning of social dynamics in complex networks |
| Grantee: | Aruane Mello Pineda |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |