2 results
Multilayer Network Science
- From Cells to Societies
- Oriol Artime, Barbara Benigni, Giulia Bertagnolli, Valeria d'Andrea, Riccardo Gallotti, Arsham Ghavasieh, Sebastian Raimondo, Manlio De Domenico
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- Published online:
- 23 August 2022
- Print publication:
- 15 September 2022
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- Element
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Networks are convenient mathematical models to represent the structure of complex systems, from cells to societies. In the last decade, multilayer network science – the branch of the field dealing with units interacting in multiple distinct ways, simultaneously – was demonstrated to be an effective modeling and analytical framework for a wide spectrum of empirical systems, from biopolymers networks (such as interactome and metabolomes) to neuronal networks (such as connectomes), from social networks to urban and transportation networks. In this Element, a decade after one of the most seminal papers on this topic, the authors review the most salient features of multilayer network science, covering both theoretical aspects and direct applications to real-world coupled/interdependent systems, from the point of view of multilayer structure, dynamics and function. The authors discuss potential frontiers for this topic and the corresponding challenges in the field for the next future.
9 - Big data: a new perspective on cities
- from Part III - Big data over social networks
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- By Riccardo Gallotti, Institut de Physique Théorique, CEA, France, Thomas Louail, Institut de Physique Théorique, CEA, France, Rémi Louf, Institut de Physique Théorique, CEA, France, Marc Barthelemy, Institut de Physique Théorique, CEA, France
- Edited by Shuguang Cui, Texas A & M University, Alfred O. Hero, III, University of Michigan, Ann Arbor, Zhi-Quan Luo, University of Minnesota, José M. F. Moura, Carnegie Mellon University, Pennsylvania
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- Book:
- Big Data over Networks
- Published online:
- 18 December 2015
- Print publication:
- 14 January 2016, pp 247-277
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- Chapter
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Summary
The recent availability of large amounts of data for urban systems opens the exciting possibility of a new science of cities. These datasets can roughly be divided into three large categories according to their time scale. We will illustrate each category by an example on a particular aspect of cities. At small time scales (of order a day or less), mobility data provided by cell phones and GPS reveal urban mobility patterns but also provide information about the spatial organization of urban systems. At very large scales, the digitalization of historical maps allows us to study the evolution of infrastructure such as road networks, and permits us to distinguish on a quantitative basis self-organized growth from top-down central planning. Finally at intermediate time scales, we will show how socio-economical series provide a nice test for modeling and identifying fundamental mechanisms governing the structure and evolution of urban systems. All these examples illustrate, at various degrees, how the empirical analysis of data can help in constructing a theoretically solid approach to urban systems, and to understand the elementary mechanisms that govern urbanization leaving out specific historical, geographical, social, or cultural factors. At this period of human history that experiences rapid urban expansion, such a scientific approach appears more important than ever in order to understand the impact of current urban planning decisions on the future evolution of cities.
Big data and urban systems
A common trait shared by all complex systems – including cities – is the existence of a large variety of processes occurring over awide range of time and spatial scales.The main obstacle to the understanding of these systems therefore resides at least in uncovering the hierarchy of processes and in singling out the few that govern their dynamics. Albeit difficult, the hierarchization of processes is of prime importance. A failure to do so leads either to modelswhich are too complex to give any real insight into the phenomenon or to be validated, or too simple to provide a satisfactory framework which can be built upon. As a matter of fact, despite numerous attempts [1–6], a theoretical understanding of many observed empirical regularities in cities is still missing. This situation is, however, changing with the recent availability of an unprecedented amount of data about cities and their inhabitants.