Foundations of Complex Systems

Synchronization processes are ubiquitous despite the many connectivity patterns that complex systems can show. Usually, the emergence of synchrony is a macroscopic observable, however, the microscopic details of the system, as e.g. the underlying network of interactions, is many times partially or totally unknown. We already know that dierent interaction structures can give rise to a common functionality, understood as a common macroscopic observable.

Recent research has led to the discovery of fundamental new phenomena in network synchronization, including chimera states, explosive synchronization, and asymmetry-induced synchronization. Each of these phenomena has thus far been observed only in systems designed to exhibit that one phenomenon, which raises the questions of whether they are mutually compatible and of what the required conditions really are. Here, we introduce a class of remarkably simple oscillator networks that concurrently exhibit all of these phenomena, thus ruling out previously assumed conditions.

This proposition focuses on the conceptual framework developed in my doctoral research in geography, which puts the landscape at the result of interactions between humans and their environment. Then the presentation will show the first results of the study resulting from a crossing between local knowledge and remote sensing in order to characterize the structure of the landscape.

Finding an optimal subset of nodes in a network that is able to disrupt the functioning of a corrupt or criminal organization or contain an epidemic or the spread of misinformation is still one of the open problems in modern network science. In this paper, we introduce the generalized network dismantling problem, which aims at finding a minimum set of nodes that, when removed from a network, results in the fragmentation of a network into sub-critical network components at minimum cost.

We analyze a class of opinion formation processes with the spreading mechanism based on the q-voter model with stochastic driving on complex networks. In the models, opinion dynamics may occur under two types of social response: conformity and anticonformity. The letter can be classify as one kind of nonconformity. The competition between interactions arising from conformity and nonconformity gives rise to order-disorder phase transitions, which are investigated by means of the pair approximation and Monte Carlo simulations.

We present a continuous formulation of epidemic spreading on multilayer networks using a tensorial representation, extending the models of monoplex networks to this context. We derive analytical expressions for the epidemic threshold of the susceptible-infected-susceptible (SIS) and susceptible-infected-recovered dynamics, as well as upper and lower bounds for the disease prevalence in the steady state for the SIS scenario.

Climate extremes and rapid urbanization are stressors that both shape and threat
ecosystems. For this purpose models that predict reliably the whole ecosystem
dynamics and species persistence time across multiple scales and species are in need.

In this paper we consider the problem of inferring a causality structure from multiple time series with asynchronous sampling by use of the Kinetic Ising Model. The problem is ubiquitous in many fields of science, ranging from neuroscience ― where spike train data from multiple neurons can carry information about connections in the brain ― to finance, where one is interested in forecasting the behavior of observable variables (such as prices and order flows) and often has to take into account multiple time varying factors.

Sampling sensitive traits in hidden populations is particularly challenging by using standard sampling methods mainly because of the lack of a sampling frame and the difficulty to obtain reliable responses. Respondent‐driven sampling is an alternative methodology that exploits the social networks between peers to reach and weight the individuals. The structure of the social contacts thus regulates the process, that can be modelled by a random walk process, by constraining the sampling within sub-regions of the network.

Applying network science to sports is gaining momentum. For example, the structure and dynamics of soccer teams have been modeled as a complex network resulting from passing interactions between players [Goncalves et al., 2017]. Typical outputs of the network modeling are the characterization of the contribution of players to the team and the distribution of passing/reception in the field. This is possible using network metrics such as the clustering coefficient and centrality metrics (closeness and betweenness).