Browsing by Author "Iniguez, Gerardo"

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  • Data-driven strategies for optimal bicycle network growth
    (2020-12-16) Natera Orozco, Luis Guillermo; Battiston, Federico; Iniguez, Gerardo; Szell, Michael
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Urban transportation networks, from pavements and bicycle paths to streets and railways, provide the backbone for movement and socioeconomic life in cities. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, it is yet unclear how to extend them comprehensively and effectively given a limited budget. Here we investigate the structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We introduce two greedy algorithms to add the most critical missing links in the bicycle network focusing on connectedness, and show that they outmatch both a random approach and a baseline minimum investment strategy. Our computational approach outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. It is a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders.
  • The effects of varying neighborhood size on the evolution of opinion in a coevolving linear chain
    (2012) Backlund, Ville-Pekka
     Perustieteiden korkeakoulu | Bachelor's thesis
  • Generic temporal features of performance rankings in sports and games
    (2016-12-01) Morales, José A.; Sánchez, Sergio; Flores, Jorge; Pineda, Carlos; Gershenson, Carlos; Cocho, Germinal; Zizumbo, Jerónimo; Rodríguez, Rosalío F.; Iniguez, Gerardo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Many complex phenomena, from trait selection in biological systems to hierarchy formation in social and economic entities, show signs of competition and heterogeneous performance in the temporal evolution of their components, which may eventually lead to stratified structures such as the worldwide wealth distribution. However, it is still unclear whether the road to hierarchical complexity is determined by the particularities of each phenomena, or if there are generic mechanisms of stratification common to many systems. Human sports and games, with their (varied but simple) rules of competition and measures of performance, serve as an ideal test-bed to look for universal features of hierarchy formation. With this goal in mind, we analyse here the behaviour of performance rankings over time of players and teams for several sports and games, and find statistical regularities in the dynamics of ranks. Specifically the rank diversity, a measure of the number of elements occupying a given rank over a length of time, has the same functional form in sports and games as in languages, another system where competition is determined by the use or disuse of grammatical structures. We use a Gaussian random walk model to reproduce the rank diversity of the studied sports and games. We also discuss the relation between rank diversity and the cumulative rank distribution. Our results support the notion that hierarchical phenomena may be driven by the same underlying mechanisms of rank formation, regardless of the nature of their components. Moreover, such regularities can in principle be used to predict lifetimes of rank occupancy, thus increasing our ability to forecast stratification in the presence of competition.
  • Kinetics of Social Contagion
    (2015) Ruan, Zhongyuan; Iniguez, Gerardo; Karsai, Márton; Kertész, János
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
  • Opinion formation on social networks with algorithmic bias: dynamics and bias imbalance
    (2021-12) Peralta, Antonio F.; Kertesz, Janos; Iniguez, Gerardo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    We investigate opinion dynamics and information spreading on networks under the influence of content filtering technologies. The filtering mechanism, present in many online social platforms, reduces individuals' exposure to disagreeing opinions, producing algorithmic bias. We derive evolution equations for global opinion variables in the presence of algorithmic bias, network community structure, noise (independent behavior of individuals), and pairwise or group interactions. We consider the case where the social platform shows a predilection for one opinion over its opposite, unbalancing the dynamics in favor of that opinion. We show that if the imbalance is strong enough, it may determine the final global opinion and the dynamical behavior of the population. We find a complex phase diagram including phases of coexistence, consensus, and polarization of opinions as possible final states of the model, with phase transitions of different order between them. The fixed point structure of the equations determines the dynamics to a large extent. We focus on the time needed for convergence and conclude that this quantity varies within a wide range, showing occasionally signatures of critical slowing down and meta-stability.