155 research outputs found

    Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users

    Get PDF
    In this paper, a homogeneous continuous time Markov chain (CTMC) is used to model information diffusion or dissemination, also to determine influencers on Twitter dynamically. The tweeting process can be modeled with a homogeneous CTMC since the properties of Markov chains are fulfilled. In this case, the tweets that are received by followers only depend on the tweets from the previous followers. Knowledge Discovery in Database (KDD) in Data Mining is used to be research methodology including pre-processing, data mining process using homogeneous CTMC, and post-processing to get the influencers using visualization that predicts the number of affected users. We assume the number of affected users follows a logarithmic function. Our study examines the Indonesian Twitter data users with tweets about covid19 vaccination resulted in dynamic influencer rankings over time. From these results, it can also be seen that the users with the highest number of followers are not necessarily the top influencer.publishedVersio

    Twitter como herramienta de comunicación científica en España. Principales agentes y redes de comunicación.

    Get PDF
    El estudio de los principales agentes, redes de comunicación y flujos de información en Twitter es un objeto de investigación emergente.Se ha aplicado en ámbitos como la comunicación política, el deporte o el turismo, pero no a la comunicación de la ciencia. El presente trabajo está enfocado a detectar y medir a los principales agentes y redes de comunicación de la ciencia en Twitter a través de la Teoría de Redes. Se identifican los 109 usuarios personales e institucionales que están ejerciendo de influencers de la ciencia en España. Esta red se presenta como una comunidad estable y compacta. Los perfiles más productivos son los personales, lo que indica que la actividad en Twitter dependemás de un interés y un compromiso individual que de disponer de un equipo de comunicación. Se detecta un uso de Twitter no tanto centrado en la difusión de contenidos y opiniones sobre ciencia, sino en la promociónde productos y eventos de divulgación. Un análisis restringido de los hashtags ha permitido comprobar la fuerte vinculación de los tuits con la actualidad científica nacional e internacional. También se evidencia el especial interés que suscita Atapuerca en las conversaciones sobre ciencia en Twitter en Españ

    The social components of innovation: from data analysis to mathematical modelling

    Get PDF
    Novelties are a key driver of societal progress, yet we lack a comprehensive understanding of the factors that generate them. Recent evidence suggests that innovation emerges from the balance between exploiting past discoveries and exploring new possibilities, the so-called ``adjacent possible". This thesis aims at developing new analysis tools and models to study how people navigate the seemingly infinite space of possibilities. Firstly, I extend the notion of the adjacent possible to account for novelties as combinations of existing elements. In particular, I model innovation as a random walk on an expanding complex network of content, in which novelties correspond not only to the first visit of nodes, but also of links. The model correctly reproduces how novelties emerge in empirical data, highlighting the importance of the exploration process in shaping the growth of the network. Secondly, since people continuously interact and exchange information with each other, I investigate the role of social interactions in enhancing discoveries. I hence propose a model where multiple agents extend their adjacent possible through the links of a complex social network, exploiting in this way opportunities coming from their contacts. By adding a social dimension to the adjacent possible, I prove that the discovery potential of an individual is influenced by its position on the social network. Finally, I combine the two concepts of the adjacent possible in the content and social dimension to develop a data-driven model of music exploration on online platforms. In such a model, multiple agents grow their individual space of possibilities by exploring a network of similarity between artists, while exploiting suggestions from their friends on the social network. The comparison with the empirical data indicates that the adjacent possible, in both the content and the social space, plays a crucial role in determining the individual propensity to innovate

    Diffusion and Supercritical Spreading Processes on Complex Networks

    Get PDF
    Die große Menge an Datensätzen, die in den letzten Jahren verfügbar wurden, hat es ermöglicht, sowohl menschlich-getriebene als auch biologische komplexe Systeme in einem beispiellosen Ausmaß empirisch zu untersuchen. Parallel dazu ist die Vorhersage und Kontrolle epidemischer Ausbrüche für Fragen der öffentlichen Gesundheit sehr wichtig geworden. In dieser Arbeit untersuchen wir einige wichtige Aspekte von Diffusionsphänomenen und Ausbreitungsprozeßen auf Netzwerken. Wir untersuchen drei verschiedene Probleme im Zusammenhang mit Ausbreitungsprozeßen im überkritischen Regime. Zunächst untersuchen wir die Reaktionsdiffusion auf Ensembles zufälliger Netzwerke, die durch die beobachteten Levy-Flugeigenschaften der menschlichen Mobilität charakterisiert sind. Das zweite Problem ist die Schätzung der Ankunftszeiten globaler Pandemien. Zu diesem Zweck leiten wir geeignete verborgene Geometrien netzgetriebener Streuprozeße, unter Nutzung der Random-Walk-Theorie, her und identifizieren diese. Durch die Definition von effective distances wird das Problem komplexer raumzeitlicher Muster auf einfache, homogene Wellenausbreitungsmuster reduziert. Drittens führen wir durch die Einbettung von Knoten in den verborgenen Raum, der durch effective distances im Netzwerk definiert ist, eine neuartige Netzwerkzentralität ein, die ViralRank genannt wird und quantifiziert, wie nahe ein Knoten, im Durchschnitt, den anderen Knoten im Netzwerk ist. Diese drei Studien bilden einen einheitlichen Rahmen zur Charakterisierung von Diffusions- und Ausbreitungsprozeßen, die sich auf komplexen Netzwerken allgemein abzeichnen, und bieten neue Ansätze für herausfordernde theoretische Probleme, die für die Bewertung künftiger Modelle verwendet werden können.The large amount of datasets that became available in recent years has made it possible to empirically study humanly-driven, as well as biological complex systems to an unprecedented extent. In parallel, the prediction and control of epidemic outbreaks have become very important for public health issues. In this thesis, we investigate some important aspects of diffusion phenomena and spreading processes unfolding on networks. We study three different problems related to spreading processes in the supercritical regime. First, we study reaction-diffusion on ensembles of random networks characterized by the observed Levy-flight properties of human mobility. The second problem is the estimation of the arrival times of global pandemics. To this end, we derive and identify suitable hidden geometries of network-driven spreading processes, leveraging on random-walk theory. Through the definition of network effective distances, the problem of complex spatiotemporal patterns is reduced to simple, homogeneous wave propagation patterns. Third, by embedding nodes in the hidden space defined by network effective distances, we introduce a novel network centrality, called ViralRank, which quantifies how close a node is, on average, to the other nodes. These three studies constitute a unified framework to characterize diffusion and spreading processes unfolding on complex networks in very general settings, and provide new approaches to challenging theoretical problems that can be used to benchmark future models

    Breadth analysis of Online Social Networks

    Get PDF
    This thesis is mainly motivated by the analysis, understanding, and prediction of human behaviour by means of the study of their digital fingeprints. Unlike a classical PhD thesis, where you choose a topic and go further on a deep analysis on a research topic, we carried out a breadth analysis on the research topic of complex networks, such as those that humans create themselves with their relationships and interactions. These kinds of digital communities where humans interact and create relationships are commonly called Online Social Networks. Then, (i) we have collected their interactions, as text messages they share among each other, in order to analyze the sentiment and topic of such messages. We have basically applied the state-of-the-art techniques for Natural Language Processing, widely developed and tested on English texts, in a collection of Spanish Tweets and we compare the results. Next, (ii) we focused on Topic Detection, creating our own classifier and applying it to the former Tweets dataset. The breakthroughs are two: our classifier relies on text-graphs from the input text and we achieved a figure of 70% accuracy, outperforming previous results. After that, (iii) we moved to analyze the network structure (or topology) and their data values to detect outliers. We hypothesize that in social networks there is a large mass of users that behaves similarly, while a reduced set of them behave in a different way. However, specially among this last group, we try to separate those with high activity, or low activity, or any other paramater/feature that make them belong to different kind of outliers. We aim to detect influential users in one of these outliers set. We propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), labeling the outliers detected os of shape, magnitude, amplitude or combination of those. We applied this method to a subset of roughly 400 million Google+ users, identifying and discriminating automatically sets of outlier users. Finally, (iv) we find interesting to address the monitorization of real complex networks. We created a framework to dynamically adapt the temporality of large-scale dynamic networks, reducing compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.PublicadoPrograma de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Rosa María Benito Zafrilla.- Secretario: Ángel Cuevas Rumín.- Vocal: José Ernesto Jiménez Merin

    DESCRIBING URGENT EVENT DIFFUSION ON TWITTER USING NETWORK STATISTICS

    Get PDF
    In this dissertation, I develop a novel framework to study the diffusion of urgent events through the popular social media platform—Twitter. Based on my literature review, this is the first comprehensive study on urgent event diffusion through Twitter. I observe similar diffusion patterns among different data sets and adopt the "cross prediction" mode to handle the early time prediction problem. I show that the statistics from the network of Twitter retweets can not only provide profound insights about event diffusion, but also can be used to effectively predict user influence and topic popularity. The above findings are consistent across various experiment settings. I also demonstrate that linear models consistently outperform state-of-art nonlinear ones in both user and hashtag prediction tasks, possibly implying the strong log-linear relationship between selected prediction features and the responses, which potentially could be a general phenomenon in the case of urgent event diffusion

    Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

    Get PDF
    Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

    Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

    Get PDF
    The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page
    corecore