Research
How does consensus emerge in the absence of a central authority? How do behaviours/ideas/opinions spread? What's the interplay between our cognition and cultural evolution? How do we shape and are shaped by cities? What's the role of social networks in all this?
These are some of the questions I am interested in, along with others in Computational Social Science (aka Social Data Science). Also thanks to fortunate collaborations with friends and colleagues in social, behavioural and cognitive sciences I have addressed them in (what I believe is) an interdisciplinary way using concepts and tools from Statistical Physics together with mathematical modelling, data science approaches and experimental work.
At a more fundamental level, my research in Network Science focuses on the properties of complex networks - especially time-varying networks - and how they affect the dynamical processes taking place upon them.
Below you can find some of the projects that have kept me busy lately (for a full list of papers please click here). For the sake of space I do not mention collaborators below but, as the papers indicate, their role has been absolutely crucial.
(constantly in need of some updates)
The emergence and dynamics of social conventions
Most of our life, from language to economics, is ruled by social conventions. I have looked at how naming conventions emerge and evolve with the Naming Game model (JSTAT 2006 and many others), and lately using a data science approach (ICWSM 2013). Recently, we have run experiments to test the hypothesis of the spontaneous emergence of conventions (PNAS 2015). The experiment results are well explained by the Naming Game model (click here for pointers to some key results).
A recent spinoff of this line of research concerns the dynamics of cryptocurrencies. Recently, we have run a thorough study of the whole cryptocurrency market from 2013 to 2017 showing that the neutral model for evolution captures key statistical properties of the data (RSOS 2017).
Broadening the perspective, I have recently put together an overview of the problem of consensus across disciplines and approaches (RSOS 2018). #modelling #data_science #experiments
Human exploration of physical and mental spaces
Physical space. How de we explore space? How does our mobility behaviour evolve in time? We have recently looked into some high resolution longitudinal datasets. After a thorough characterisation of the statistical properties of human mobility (PLOS ONE 2016), we have shown that while our set of preferred locations evolves in time (arxiv 2016), the size of this set is constant (a bit like a Dunbar's number for space). #data_science #modelling
Mental spaces. How do we mentally search metric spaces, such as the 1-d line of integers? Analysing data from online auctions, we showed that users do Lévy flights, like foraging animals in nature (PLOS ONE 2012, PRE 2013). My goal is now to develop experiments to investigate human mental exploration more deeply. #data_science #modelling (#experiments coming soon, hopefully!)
Data Science and Societal Trends
Analysing Twitter data, we showed that geotagging techniques allow to accurately map world languages (PLOS ONE 2013), and user activity on Twitter allows to anticipate the result of massively popular TV shows such as American Idol (EPJ Data Science 2012). Further studies will concern individual behavioural patterns. #data_science
The emergence of linguistic categories and the interplay between culture and cognition
Categories are fundamental for our understanding of the world. How do categories emerge? And how do we keep our categories aligned through language? We have looked at this issue through a computational model that reproduces empirical data on colour naming patterns (PNAS 2008 and PNAS_2010), and clarifies how culture and biology shape our categories (Plos ONE 2015). #modelling #data_science
Modelling Face-To-Face Contact Networks
How do we behave in social gatherings? Recent data have shown universal, context-independent, patterns. We proposed a very simple model that reproduces most of the empirical observations (PRL 2013, Social Networks 2016, Sci. Rep. 2016). We are currently extending our analysis to more and more datasets. #modelling #data_science
Network Science and Dynamical Processes on Networks
I have studied simple dynamics on networks for a long time, ranging from spreading phenomena (random walks, epidemics, etc) to opinion dynamics models (voter, Moran, Naming Game, etc). Recently, I have been focusing on the structure and impact of time-varying networks (eg PRL 2012, PRL 2013, Sci. Rep. 2017, etc). #modelling
