92 research outputs found

    FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS

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    In this thesis we address the close interplay among mobility, offline relationships and online interactions and the related human networks at different dimensional scales and temporal granularities. By generally adopting a data-driven approach, we move from small datasets about physical interactions mediated by human-carried devices, describing small social realities, to large-scale graphs that evolve over time, as well as from human mobility trajectories to face-to-face contacts occurring in different geographical contexts. We explore in depth the relation between human mobility and the social structure induced by the overlapping of different people's trajectories on GPS traces collected in urban and metropolitan areas. We define the notions of geo-location and geo-community which are operational in describing in a unique framework both spatial and social aspects of human behavior. Through the concept of geo-community we model the human mobility adopting a bipartite graph. Thanks to this graph representation we can generate a social structure that is plausible w.r.t. the real interactions. In general the modeling approach have the merit for reporting the mobility in a graph-theoretic framework making the study of the interplay mobility/sociality more affordable and intuitive. Our modeling approach also results in a mobility model, Geo-CoMM, which lies on and exploits the idea of geo-community. The model represents a particular instance of a general framework we provide. A framework where the social structure behind the preferred-location based mobility models emerges. We validate Geo-CoMM on spatial, temporal, pairwise connectivity and social features showing that it reproduces the main statistical properties observed in real traces. As concerns the offline/online interplay we provide a complete overview of the close connection between online and offline sociality. To reach our goal we gather data about offline contacts and social interactions on Facebook of a group of students and we propose a multidimensional network analysis which allows us to deeply understand how the characteristics of users in the distinct networks impact each other. Results show how offline and Facebook friends are different. This way we confirm and worsen the general intuition that online social networks have shifted away from their original goal to mirror the offline sociality of individuals. As for the role and the social importance, it becomes apparent that social features such as user popularity or community structure do not transfer along social dimensions, as confirmed by our correlation analysis of the network layers and by the comparison among the communities. In the last chapters we analyze the evolution of the online social network from a physical time perspective, i.e. considering the graph evolution as a graph time-series and not as a function of the network basic properties (number of nodes or links). As for the physical time in a user-centric viewpoint, we investigate the bursty nature of the link creation process in online social network. We prove not only that it is a highly inhomogeneous process, but also identify patterns of burstiness common to all nodes. Then we focus on the dynamic formation of two fundamental network building components: dyads and triads. We propose two new metrics to aid the temporal analysis on physical time: link creation delay and triangle closure delay. These two metrics enable us to study the dynamic creation of dyads and triads, and to highlight network behavior that would otherwise remain hidden. In our analysis, we find that link delays are generally very low in absolute time and are largely independent of the dates people join the network. To highlight the social nature of this metric, we introduce the term \textit{peerness} to quantify how well linked users overlap in lifetimes. As for triadic closure delay we first introduce an algorithm to extract of temporal triangle which enables us to monitor the triangle formation process, and to detect sudden changes in the triangle formation behavior, possibly related to external events. In particular, we show that the introduction of new service functionalities had a disruptive impact on the triangle creation process in the network

    INFERRING SOCIAL NETWORKS FROM PASSIVELY COLLECTED WI-FI METADATA

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    The emergence of smartphones and other highly portable Wi-Fi enabled devices offers unprecedented amounts of information leaked through Wi-Fi metadata. The constantly connected nature of Wi-Fi devices together with the intimate relationship between users and their device presents an opportunity for using a user’s device to gain information about the user themselves. Through passive data collection, without interference or the possibility of being detected, it is possible to harvest large datasets. This work looks at the possibility of inferring underlying social networks through the analysis of these metadata traces. Using spatiotemporal proximity as an indicator of friendship, findings demonstrate the ability to accurately predict underlying social structures in various simulated settings

    INFERRING SOCIAL NETWORKS FROM PASSIVELY COLLECTED WI-FI METADATA

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    The emergence of smartphones and other highly portable Wi-Fi enabled devices offers unprecedented amounts of information leaked through Wi-Fi metadata. The constantly connected nature of Wi-Fi devices together with the intimate relationship between users and their device presents an opportunity for using a user’s device to gain information about the user themselves. Through passive data collection, without interference or the possibility of being detected, it is possible to harvest large datasets. This work looks at the possibility of inferring underlying social networks through the analysis of these metadata traces. Using spatiotemporal proximity as an indicator of friendship, findings demonstrate the ability to accurately predict underlying social structures in various simulated settings

    Human mobility and social ties in context: from places to personality

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    Recent years saw an increasing proliferation of the use of digitally generated traces of data for understanding human behaviour. The quantitative understanding of social networks as well as patterns of human mobility benefited tremendously from these new sources of data. The main dynamics of both social networks and human mobility such as a propensity of humans for heterogeneous behaviour, how humans choose to explore new places, or the fact that both spheres are intrinsically linked are now fairly well understood. However, how various other factors mediate the observed dynamics is still relatively unknown, not least due to the difficulty in obtaining adequate data. Thus, for my thesis I focus on how a variety of factors---places, longer-term dynamics, the personality of individuals, or neighbourhoods---might be a driver of various aspects of social and mobility behaviour. I used data from the Copenhagen network study that tracked 847 students with smartphones and measured their social encounters as well as the locations they visited for a whole academic year. I further utilised a variety of methods for analysing the data ranging from applied machine learning over inferential statistics to social network analysis. Using this dataset, I found that the qualities of places were very informative for understanding future encounters between students, that the longer-term dynamics shaped both social and mobility behaviour, and that while personality had a significant effect on the observed regularity of behaviour, its effect was rather small

    A network model of mass media opinion dynamics

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    The coexistence of diverse opinions is necessary for a pluralistic society in which people can confront ideas and make informed choices. The media functions as a primary source of information, and diversity across news sources in the media forms the basis for wider discourse in the public. However, due to numerous economic and social pressures, news sources frequently co-orient their content through what is known as intermedia agenda-setting. Past research on the subject has examined relationships between individual news sources. However, to understand emergent behaviour such as opinion diversity, we cannot simply analyse individual relationships in isolation, but instead need to view the media as a complex system of many interacting entities. The aim of this thesis is to develop and empirically test a method for understanding the network effects that intermedia agenda-setting has on the diversity of expressed opinions within the media. Utilising latent signals extracted from news articles, we put forward a methodology for inferring networks that capture how agendas propagate between news sources via the opinions they express on various topics. By applying this approach to a large dataset of news articles published by globally and locally prominent news organisations, we identify how the structure of intermedia networks is indicative of the level of opinion diversity across various topics. We then develop a theoretical model of opinion dynamics in noisy domains that is motivated by the empirical observations of intermedia agenda formation. From this, we derive a general analytical expression for opinion diversity that holds for any network and depends on the network's topology through its spectral properties alone. Finally, we validate the analytical expression in a linear model against empirical data. This thesis aids our understanding of how to model emergent behaviour of the media and promote diversity

    Micro-, Meso- and Macro-Connectomics of the Brain

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    Neurosciences, Neurolog

    Synchronization in Complex Networks Under Uncertainty

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    La sincronització en xarxes és la música dels sistemes complexes. Els ritmes col·lectius que emergeixen de molts oscil·ladors acoblats expliquen el batec constant del cor, els patrons recurrents d'activitat neuronal i la sincronia descentralitzada a les xarxes elèctriques. Els models matemàtics són sòlids i han avançat significativament, especialment en el problema del camp mitjà, on tots els oscil·ladors estan connectats mútuament. Tanmateix, les xarxes reals tenen interaccions complexes que dificulten el tractament analític. Falta un marc general i les soluciones existents en caixes negres numèriques i espectrals dificulten la interpretació. A més, la informació obtinguda en mesures empíriques sol ser incompleta. Motivats per aquestes limitacions, en aquesta tesi proposem un estudi teòric dels oscil·ladors acoblats en xarxes sota incertesa. Apliquem propagació d'errors per predir com una estructura complexa amplifica el soroll des dels pesos microscòpics fins al punt crític de sincronització, estudiem l'efecte d'equilibrar les interaccions de parelles i d'ordre superior en l'optimització de la sincronia i derivem esquemes d'ajust de pesos per mapejar el comportament de sincronització en xarxes diferents. A més, un desplegament geomètric rigorós de l'estat sincronitzat ens permet abordar escenaris descentralitzats i descobrir regles locals òptimes que indueixen transicions globals abruptes. Finalment, suggerim dreceres espectrals per predir punts crítics amb àlgebra lineal i representacions aproximades de xarxa. En general, proporcionem eines analítiques per tractar les xarxes d'oscil·ladors en condicions sorolloses i demostrem que darrere els supòsits predominants d'informació completa s'amaguen explicacions mecanicistes clares. Troballes rellevants inclouen xarxes particulars que maximitzen el ventall de comportaments i el desplegament exitós del binomi estructura-dinàmica des d'una perspectiva local. Aquesta tesi avança la recerca d'una teoria general de la sincronització en xarxes a partir de principis mecanicistes i geomètrics, una peça clau que manca en l'anàlisi, disseny i control de xarxes neuronals biològiques i artificials i sistemes d'enginyeria complexos.La sincronización en redes es la música de los sistemas complejos. Los ritmos colectivos que emergen de muchos osciladores acoplados explican el latido constante del corazón, los patrones recurrentes de actividad neuronal y la sincronía descentralizada de las redes eléctricas. Los modelos matemáticos son sólidos y han avanzado significativamente, especialmente en el problema del campo medio, donde todos los osciladores están conectados entre sí. Sin embargo, las redes reales tienen interacciones complejas que dificultan el tratamiento analítico. Falta un marco general y las soluciones en cajas negras numéricas y espectrales dificultan la interpretación. Además, las mediciones empíricas suelen ser incompletas. Motivados por estas limitaciones, en esta tesis proponemos un estudio teórico de osciladores acoplados en redes bajo incertidumbre. Aplicamos propagación de errores para predecir cómo una estructura compleja amplifica el ruido desde las conexiones microscópicas hasta puntos críticos macroscópicos, estudiamos el efecto de equilibrar interacciones por pares y de orden superior en la optimización de la sincronía y derivamos esquemas de ajuste de pesos para mapear el comportamiento en estructuras distintas. Una expansión geométrica del estado sincronizado nos permite abordar escenarios descentralizados y descubrir reglas locales que inducen transiciones abruptas globales. Por último, sugerimos atajos espectrales para predecir puntos críticos usando álgebra lineal y representaciones aproximadas de red. En general, proporcionamos herramientas analíticas para manejar redes de osciladores en condiciones ruidosas y demostramos que detrás de las suposiciones predominantes de información completa se ocultaban claras explicaciones mecanicistas. Hallazgos relevantes incluyen redes particulares que maximizan el rango de comportamientos y la explicación del binomio estructura-dinámica desde una perspectiva local. Esta tesis avanza en la búsqueda de una teoría general de sincronización en redes desde principios mecánicos y geométricos, una pieza clave que falta en el análisis, diseño y control de redes neuronales biológicas y artificiales y sistemas de ingeniería complejos.Synchronization in networks is the music of complex systems. Collective rhythms emerging from many interacting oscillators appear across all scales of nature, from the steady heartbeat and the recurrent patterns in neuronal activity to the decentralized synchrony in power-grids. The mathematics behind these processes are solid and have significantly advanced lately, especially in the mean-field problem, where oscillators are all mutually connected. However, real networks have complex interactions that difficult the analytical treatment. A general framework is missing and most existing results rely on numerical and spectral black-boxes that hinder interpretation. Also, the information obtained from measurements is usually incomplete. Motivated by these limitations, in this thesis we propose a theoretical study of network-coupled oscillators under uncertainty. We apply error propagation to predict how a complex structure amplifies noise from the link weights to the synchronization onset, study the effect of balancing pair-wise and higher-order interactions in synchrony optimization, and derive weight-tuning schemes to map the synchronization behavior of different structures. Also, we develop a rigorous geometric unfolding of the synchronized state to tackle decentralized scenarios and to discover optimal local rules that induce global abrupt transitions. Last, we suggest spectral shortcuts to predict critical points using linear algebra and network representations with limited information. Overall, we provide analytical tools to deal with oscillator networks under noisy conditions and prove that mechanistic explanations were hidden behind the prevalent assumptions of complete information. Relevant finding include particular networks that maximize the range of behaviors and the successful unfolding of the structure-dynamics interplay from a local perspective. This thesis advances the quest of a general theory of network synchronization built from mechanistic and geometric principles, a key missing piece in the analysis, design and control of biological and artificial neural networks and complex engineering systems

    The diffusion of environmental innovations: a geographical perspective on lead markets and technology licensing in China

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    The consequences of the climate crisis for life on earth are already severe. In addition, the planet is facing a multitude of other negative environmental impacts of human activity, including the loss of biodiversity, the depletion of resources and environmental pollution, to name but a few. In response to these great challenges, there has been extensive research over the last few years directed towards the development of environmentally friendly technologies. These so-called green technologies or environmental innovations include, among other things, renewable energy technologies, energy-efficiency technologies and waste management technologies. In order to tackle the climate crisis and environmental damage, however, it is not sufficient simply to invent green technologies; they must also be used and diffused globally. In this dissertation, I shed light on the diffusion of environmental innovations from a spatial perspective, i.e. the process from invention to adoption and the geography thereof. My research focuses on diffusion processes taking place in China, which is a particularly important case. The pace of China's sustainability transition will have a decisive impact on global futures given its current environmentally adverse modes of production and consumption. At the same time, China ranks as the largest market for green technologies and leads the technological frontier in many domains. From an economic geography point of view, many Chinese regions therefore enjoy great prospects for green regional path development, which might lead to a win-win situation for the environment and the local economy. Against this background, I analyze the diffusion of environmental innovations in Chinese regions using a regional case study and quantitative analyses of patent licensing data. Drawing on the case study, I develop a conceptual framework that provides a rationale for the spatial diffusion of environmental innovations: the regional lead market framework. The quantitative analyses provide statistical evidence for how spatial patterns of green technology diffusion might evolve into lead market structures. The findings reveal, inter alia, that geographic proximity between innovators and adopters not only increases the likelihood of innovation diffusion processes, but also their speed. The results of this dissertation yield important lessons for regional eco-innovation policy.Die Klimakrise hat gravierende Folgen für das Leben auf der Erde. Darüber hinaus ist der Planet mit einer Vielzahl weiterer negativer Umweltauswirkungen menschlichen Handelns konfrontiert, darunter der Verlust der Biodiversität, die Erschöpfung von Ressourcen sowie Umweltverschmutzungen, um nur einige zu nennen. Als Reaktion auf diese Herausforderungen wurde in den letzten Jahren umfangreich an umweltfreundlichen Technologien geforscht. Zu diesen sogenannten grünen Technologien bzw. Umweltinnovationen gehören beispielsweise regenerative Energiequellen, Energieeffizienztechnologien oder Abfallbehandlungstechnologien. Um der Klimakrise und Umweltschäden entgegenzuwirken, reicht es jedoch nicht aus, grüne Technologien zu entwickeln, sondern sie müssen auch weltweit eingesetzt werden und diffundieren. In dieser Dissertation untersuche ich die Diffusion von Umweltinnovationen aus räumlicher Perspektive, d.h. ich analysiere den Prozess von der Erfindung bis zur Anwendung von grünen Technologien sowie die Geographien dessen. Meine Forschung konzentriert sich auf Diffusionsprozesse in China, da China aufgrund der gegenwärtigen umweltschädlichen Produktions- und Konsumweisen eine kritische Rolle für die globale Nachhaltigkeitstransition spielt. China ist gleichzeitig auch wichtigster Markt für grüne Technologien und in vielen umweltrelevanten Bereichen technologisch führend. Aus wirtschaftsgeographischer Perspektive bieten sich daher für viele chinesische Regionen vielversprechende Chancen für eine grüne Regionalentwicklung, die positive Effekte sowohl für die Umwelt als auch für die lokale Wirtschaft und Bevölkerung miteinander vereinbart. Vor diesem Hintergrund analysiere ich die Diffusion von Umweltinnovationen in chinesischen Regionen anhand von einer regionalen Fallstudie und quantitativen Analysen von Technologielizenzen zu grünen Patenten. Auf Basis der Fallstudie erarbeite ich das Konzept der Regionalen Leitmärkte, das eine Begründung für die räumliche Verbreitung von Umweltinnovationen liefert. Die quantitativen Analysen präsentieren statistische Hinweise dafür, wie sich räumliche Diffusionsmuster grüner Technologien zu Leitmarktstrukturen entwickeln können. Die Ergebnisse zeigen unter anderem, dass die räumliche Nähe zwischen Innovatoren und Anwendern nicht nur die Wahrscheinlichkeit von Innovationsdiffusionsprozessen erhöht, sondern auch deren Geschwindigkeit. Die Ergebnisse dieser Dissertation liefern wichtige Erkenntnisse für die regionale Umweltinnovationspolitik
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