Much of the structure in social networks has been explained by two seemingly independent network evolution mechanisms: triadic closure and homophily. While it is common to consider these mechanisms separately or in the frame of a static model, empirical studies suggest that their dynamic interplay is the very process responsible for the homophilous patterns of association seen in off-and online social networks. By combining these two mechanisms in a minimal solvable dynamic model, we confirm theoretically the long-held and empirically established hypothesis that homophily can be amplified by the triadic closure mechanism. This research approach allows us to estimate how much of the observed homophily in various friendship and communication networks is due to amplification for a given amount of triadic closure. We find that the cumulative advantage-like process leading to homophily amplification can, under certain circumstances, also lead to the widely documented core-periphery structure of social networks, as well as to the emergence of memory of previous homophilic constraints (equivalent to hysteresis phenomena in physics). The theoretical understanding provided by our results highlights the importance of early intervention in managing at the societal level the most adverse effects of homophilic decision-making, such as inequality, segregation and online echo chambers.
The last centuries have witnessed a great surge in our understanding and control of ‘simple’ physical, chemical, and biological processes through data analysis and the mathematical modelling of their underlying dynamics. Encouraged by this success, researchers have recently tried to do the same for social, ecological, and economic systems, thanks to the massive data generated by information-communication technologies and the unprecedented fusion of off- and online human activity. However, due to the presence of adaptability, feedback loops, and strong heterogeneities in the elements and interactions comprising our modern digital societies, it is unclear if statistical ‘laws’ of socio-technical behaviour even exist, akin to those found for natural processes. This continuing search has resulted in the fields of network science and computational social science, which share the goal of modelling social phenomena with enough accuracy to make reliable predictions. In this talk I summarise some of my earlier and ongoing contributions to these fields, dealing with social influence in the spreading of Skype and other social networks, the evolution of social conflicts in collaborative platforms like Wikipedia, and the persistent temporal features of hierarchies in socio-technical systems. I also discuss possibilities to continue this research, such as more realistic models of social dynamics, the use of statistical inference and machine learning techniques to compare idealised models, and the creation of loops between data acquisition and model analysis to increase prediction accuracy. All of this with the goal of contributing to a ‘science of digital societies’, which will be able to provide quantitative tools to better understand the complex societies of our day
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.