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34 result(s)
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Abstract

The field we know today as Network Science had its beginning in the analysis of large database setswith the initial goal in mapping the connectivity pattern of real complex systems. The great finding ofthe early works in this context was the discovery that systems of different nature exhibited highly nontrivialstructural patterns, such as the emergence of scale-free properties and hierarchical organizations ofits elements. These results were then followed by countless works focused in the development and formalizationof metrics devoted to the structural characterization of these systems. However, in many cases, thetopological description of a given system does not provide all resources to thoroughly assess the system'sevolution. Once the structure has been quantified, one still needs to establish the connection between thenetwork topology and dynamical processes taking place on it. Unfortunately, progress in this directionturned out to be much more slower than in the structural characterization of networks. Indeed, analyticalresults concerning dynamical processes in these structures are hard, or even impossible, to be obtained;especially in topologies that present characteristics found in real networks. It is in this context that lies themotivation of this project, namely to study dynamical processes in complex networks that exhibit topologiesakin to the ones observed in real systems. To this end, we will study epidemic and synchronizationprocesses considering random graph models that generate networks that incorporate structural propertiesobserved empirically. With this approach, we aim at quantifying the influence of topological patternsusually neglected on the dynamics of real complex systems. Finally, the analysis developed firstly for randommodels will be later applied to the study of real-world networks whose functioning is described bythe dynamical processes under investigation in the project, such as power-grid networks in the context ofsynchronization, and social networks under the influence of endemic and rumour spreading processes. (AU)

Abstract

Complex networks are essential tools to model propagation processes in society, such as epidemics and rumors. The study of such processes allows the prediction of the spread of diseases in the real world, as well as the development of effective immunization strategies. However, most network models do not consider the diversity of interactions between individuals, nor the influence of knowledge about the disease in its propagation. These aspects can only be modeled with the multilayer network formalism, which has attracted great attention in the last years. This project proposes the study of propagation processes in multilayer networks. The influence of the network structure on the propagation process will be analyzed, considering diversities in the transmission routes, types of social contact and susceptibility of infection in each individual. Finally, we shall study the coupling between epidemics and rumors spreading, in which the information about the epidemic can impact its propagation. This study allows the planning of efficient immunization and information campaigns, taking into account the multilayer structure of the interactions. (AU)

Abstract

Complex networks represent the structure of complex systems, from biological interactions to social contacts. The study of these networks has growth in importance during the last years and has allowed to model the organization and dynamics of several systems. However, recently, the traditional network analysis was generalized to consider the multilayer organization, which are composed by interconnected networks. This modeling has been applied in the investigation of several dynamical processes taking place on the top of networks, such as epidemic spreading and synchronization. In this project, we aim to explore the analysis of synchronization and epidemic spreading in multilayer networks, as well as World Trade network data. These works are developed in collaboration between the Institute for Biocomputation and Physics of Complex Systems (at Universidad de Zaragoza) and the Complex Systems group (at the Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo - ICMC-USP). In this project, we describe the main activities to be executed during the visit of prof. Yamir Moreno to ICMC-USP, which will be fundamental to reinforce our collaboration and discuss new possible works. (AU)

Abstract

Epidemic spreading can follow different routes, which can be mapped as layers in social networks. A great challenge in computational epidemics todays is to determine how the structure of the network influences the propagation. In this project, we aim at applying statistical inference methods for quantification of which network properties most influence the propagation of disease and rumors. In addition, we will model the social organization as made up by several layers. Finally, we will study how awareness influences the propagation of an infectious agent. These studies will contribute for developing new methods for epidemic forecasting and control. (AU)

Abstract

There has been a remarkable increasing in the amount of stored data by private and public companies about urban systems. On one hand, these huge amounts of data enable a detailed historical review of the processes under investigation; on the other hand, this excess of data makes harder to extract summarized information and also to make good decisions supported by well-established empirical facts. Cities are complex systems in which the equations governing their dynamics depend on several social, economical, and environmental factors. A theory explaining how these attributes of cities influence cities performance would help to inform urban planning and development. It is clear that this is a timely and on the rise theme with many challenges that deserve attention from scientific community. In order to understand, investigate and extract patterns from cities, researchers usually need a wide range of skills such as those from Statistical Physics, Mathematics, Statistics, and Computer Science. In the course of this project, we intend to investigate several aspects of urban systems by employing the Statistical Physics and omputer Science framework. Specifically, the problems that must be addressed in this project are: i) The role of commuters in urban scaling; ii) Long-term migrations and its effects on urban scaling; iii) What are the mechanism that generate the fluctuations in the scaling laws? iv) How does population density affect urban indicators? v) Can the spread of crime be modelled by epidemic models? vi) Modelling urban systems via complex networks and multilayer networks. We believe that these studies may have potential applications in the allocation of new policies and resource in the context of urban planning and development. (AU)

Abstract

In this project, we propose the analysis of the World Trade Web as a multilayer network. We will consider the import-export relationships of each product as a single layer and verify which is the most suitable way to connect the layers. Methods for layer reduction will be considered in order to obtain a more compact, but still a representative organization of the WTW. (AU)

Abstract

The network theory has been developed since the end of the last century. Although several advances have been obtained, there are numerous challenges to be overcome. One of the most important is related to the limitation in the processing of networks made of million of vertices. Since the libraries currently available consider sequential processing, the calculation of some measures is very limited. In this project, we aim at developing several parallel functions for calculation of network measures. Basically, we will implement measures related to distance, centrality, spectral properties and community detection. These new function will enable the characterization of networks make up by dozen of millions of vertices, which can be done with supercomputer currently. (AU)

Abstract

Epidemics propagation models are fundamental to prevent how an infectious agent spreads in a society. In the literature, different models were proposed, such as the susceptible-infected-susceptible and the susceptible-infected-recovered. Most models reckons the disease spreads in a society with no reaction from its individuals. However, together with the infectious agent, the epidemic's information also spreads in this society. Therefore, recently, a wide variety of researches were proposed to model this epidemiologic model with alert. In this project, we aim to study those models and verify how the network's topology, as well as the alert propagation methods affect the final portion of infected individuals. This study is essential to understand how information's politics can be developed to lessen an epidemicEpidemics propagation models are fundamental to prevent how an infectious agent spreads in a society. In the literature, different models were proposed, such as the susceptible-infected-susceptible and the susceptible-infected-recovered. Most models reckons the disease spreads in a society with no reaction from its individuals. However, together with the infectious agent, the epidemic's information also spreads in this society. Therefore, recently, a wide variety of researches were proposed to model this epidemiologic model with alert. In this project, we aim to study those models and verify how the network's topology, as well as the alert propagation methods influence the final portion of infected individuals. This study is essential to understand how information's politics can be developed to lessen an epidemic impact's impact. (AU)

Abstract

The theory of complex networks is an interdisciplinary research field, which is applied in several areas ofscience, such as sociology, biology, engineering and economics. In the case of the economy, it is possible torepresent the world trade by considering the vertices as countries and interactions, as business transactions.In this project, we aim at building networks of global trade and consider methods and concepts of the theoryof complex networks in order to characterize the topology of the network. We will model the world trade asmultilayer networks and analyze how the transfer of goods between countries occurs. These studies shouldhelp in a better understanding of the structure and dynamics of world trade. (AU)

Abstract

Propagation models of epidemics and rumors are essential for predicting and controlling the transmission of infectious agents and social behavior.Most models developed in complex networks only considers static structures where connections are maintained over time.Only recently, epidemic propagation models in adaptative networks have been proposed. In these networks, the connections are not static, varying during the spread of the infectious agent. In this study, we will aim to study such models and propose new network adaptation rules. Rumors models will also be considered. The results will allow a better understanding of the spread of information in complex networks with time-varying structure. (AU)

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