Advances in Complex Systems

Social systems are characterized by the presence of group interactions and by the existence of both trust and distrust relations. Although there is a wide literature on signed social networks, where positive signs associated to the links indicate trust, friendship, agreement, while negative signs represent distrust, antagonism, and disagreement, very little is known about the effect that signed interactions can have on the spreading of social behaviors when, not only pairwise, but also higher-order interactions are taken into account. In this paper, we introduce a model of complex contagion on signed simplicial complexes, and we investigate the role played by trust and distrust on the dynamics of a social contagion process, where exposure to multiple sources is needed for the contagion to occur. The presence of higher-order signed structures in our model naturally induces new infection and recovery mechanisms, thus increasing the richness of the contagion dynamics. Through numerical simulations and analytical results in the mean-field approximation, we show how distrust determines the way the system moves from a state where no individuals adopt the social behavior, to a state where a finite fraction of the population actively spreads it. Interestingly, we observe that the fraction of spreading individuals displays a non-monotonic dependence with respect to the average number of connections between individuals. We then investigate how social balance affects social contagion, finding that balanced triads have an ambivalent impact on the spreading process, either promoting or impeding contagion based on the relative abundance of fully trusted relations. Our results shed light on the nontrivial effect of trust on the spreading of social behaviors in systems with group interactions, paving the way to further investigations of spreading phenomena in structured populations.

Bibliometric studies based on the Web of Science (WOS) database have become an increasingly popular method for analyzing the structure of scientific research. So do network approaches, which, based on empirical data, make it possible to characterize the emergence of topological structures over time and across multiple research areas. Our paper is a contribution to interweaving these two lines of research that have progressed in separate ways but whose common applications have been increasingly frequent. Among other attributes, Author Keywords and Keywords Plus® are used as units of analysis that enable us to identify changes in the topics of interest and related bibliography. By considering the co-occurrence of those keywords with the Author Keyword Complexity, we provide an overview of the evolution of studies on Complexity Sciences, and compare this evolution in seven social and natural scientific fields. The results show a considerable increase in the number of papers dealing with complexity, as well as a general tendency across different disciplines for this literature to move from a more foundational, general and conceptual to a more applied, specific and empirical set of co-occurring keywords. Moreover, we provide evidence of changing topologies of networks of co-occurring keywords, which are described through the computation of some topological coefficients. In so doing, we emphasize the distinguishing structures that characterize the networks of the seven research areas.

This paper studies cluster dependence and synchronization in multilayer directed networks. Based on network quotient graphs, we provide a more efficient way to identify dependencies between clusters. By considering the relationships and interactions between clusters at a higher level of abstraction, this analysis can reduce the computational effort without requiring extensive computation on the original network. Furthermore, this paper investigates the impact of structural perturbations on network synchronization capabilities within a multilayer framework. Specifically, for the case where perturbations are applied to the dominant group, we study how changes in the network structure affect its cluster synchronization (CS) behavior. Finally, we employe a multilayer neural network model to validate the findings presented in this paper.