Taxes, Inequality, and Equal Opportunities.

Extreme inequality represents a grave challenge for impoverished individuals and poses a threat to economic growth and stability. Despite the fulfillment of affirmative action measures aimed at promoting equal opportunities, they often prove inadequate in effectively reducing inequality. Mathematical models and simulations have demonstrated that even when equal opportunities are present, wealth tends to concentrate in the hands of a privileged few, leaving the majority of the population in dire poverty.

Machine learning partners in criminal networks.

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy.

Universality of political corruption networks.

Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades.

Study of taxes, regulations and inequality using machine learning algorithms.

Genetic machine learning (ML) algorithms to train agents in the Yard-Sale model proved very useful for finding optimal strategies that maximize their wealth. However, the main result indicates that the more significant the fraction of rational agents, the greater the inequality at the collective level. From social and economic viewpoints, this is an undesirable result since high inequality diminishes liquidity and trade.

Macroscopic and microscopic perspectives for adoption of technologies in the USA.

Adoption of a new technology depends on many factors. Marketing, advertising, social interactions, and personal convictions are relevant features when deciding to adopt, or not, a new technology. Thus, it is very important to determine the relative weight of these factors when introducing a new technology.

Topology, robustness, and structural controllability of the Brazilian Federal Police criminal intelligence network.

Law enforcement and intelligence agencies worldwide struggle to find effective ways to fight organized crime and reduce criminality. However, illegal networks operate outside the law and much of the data collected is classified. Therefore, little is known about the structure, topological weaknesses, and control of criminal networks.

Fast Fragmentation of Networks Using Module-Based Attacks.

In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from a practical point of view. In this contribution, we present a module-based method to efficiently fragment complex networks. The procedure firstly identifies topological communities through which the network can be represented using a well established heuristic algorithm of community finding.

Anomalous diffusion in the evolution of soccer championship scores: real data, mean-field analysis, and an agent-based model.

Statistics of soccer tournament scores based on the double round robin system of several countries are studied. Exploring the dynamics of team scoring during tournament seasons from recent years we find evidences of superdiffusion. A mean-field analysis results in a drift velocity equal to that of real data but in a different diffusion coefficient.

Epidemics scenarios in the "Romantic network".

The networks of sexual contacts together with temporal interactions play key roles in the spread of sexually transmitted infections. Unfortunately, data for this kind of network is scarce. One of the few exceptions, the "Romantic network", is a complete structure of a real sexual network in a high school.