Riccardo Di Clemente and Iacopo Iacopini
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Sequences of purchases in credit card data reval lifestyles in urban populations:

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.

Simplicial models of social contagion

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterize social contagion processes such as opinion formation or the adoption of novelties, where complex mechanisms of influence and reinforcement are at work. Here we introduce a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes. Numerical simulations of the model on both empirical and synthetic simplicial complexes highlight the emergence of novel phenomena such as a discontinuous transition induced by higher-order interactions. We show analytically that the transition is discontinuous and that a bistable region appears where healthy and endemic states co-exist. Our results help explain why critical masses are required to initiate social changes and contribute to the understanding of higher-order interactions in complex systems.

About the speaker
About the speaker
Riccardo Di Clemente is an Associate Professor in the Network Science Institute at Northeastern University London. Riccardo develops mathematical frameworks to analyze and model the complex social connections that govern human behavior and interactions within cities and online. His research utilizes network theory, complex systems computational social science, and machine learning methods to investigate the digital footprints left by individuals in their daily routines. Iacopo Iacopini is an Assistant Professor in the Network Science Institute at Northeastern University London. His research interests are in the area of complex networks and computational social sciences, with a focus on behavioural contagion, discovery processes, group and team dynamics.
Riccardo Di Clemente is an Associate Professor in the Network Science Institute at Northeastern University London. Riccardo develops mathematical frameworks to analyze and model the complex social connections that govern human behavior and interactions within cities and online. His research utilizes network theory, complex systems computational social science, and machine learning methods to investigate the digital footprints left by individuals in their daily routines. Iacopo Iacopini is an Assistant Professor in the Network Science Institute at Northeastern University London. His research interests are in the area of complex networks and computational social sciences, with a focus on behavioural contagion, discovery processes, group and team dynamics.