1,720,964 research outputs found
Detecting network backbones against time variations in node properties
Full text linkMany real systems can be described through time-varying networks of interactions that encapsulate information sharing between individual units over time. These interactions can be classified as being either reducible or irreducible: reducible interactions pertain to node-specific properties, while irreducible interactions reflect dyadic relationships between nodes that form the network backbone. The process of filtering reducible links to detect the backbone network could allow for identifying family members and friends in social networks or social structures from contact patterns of individuals. A pervasive hypothesis in existing methods of backbone discovery is that the specific properties of the nodes are constant in time, such that reducible links have the same statistical features at any time during the observation. In this work, we release this assumption toward a new methodology for detecting network backbones against time variations in node properties. Through analytical insight and numerical evidence on synthetic and real datasets, we demonstrate the viability of the proposed approach to aid in the discovery of network backbones from time series. By critically comparing our approach with existing methods in the technical literature, we show that neglecting time variations in node-specific properties may beget false positives in the inference of the network backbone
On the dynamics of autocatalytic cycles in protocell models
No full textThe emergence of autocatalytic sets of molecules seems to have played an important role in the origin of life, allowing a sustainable systems’ growth and reproduction. Several frameworks have been proposed, one of the most recent and promising being that of RAF (Reflexively Autocatalytic-Food generated) sets. As it often happens when topological properties only are taken into account, RAFs are however only potentially able of supporting continuous growth. Dynamics can also play a significant role: it is shown here how dynamical interactions may sometimes lead to unexpected behaviors
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Analysis and Inference in Temporal Networks, with Application to Epidemic Spreading
No full textThe development of accurate predictions of the spread of real-world diseases requires an interdisciplinary effort. Epidemiology, social sciences, and network science are the three fields that are mostly involved in this research area. Epidemiology studies how a disease evolves and spreads in the human population. It investigates the diffusion mechanism of a pathogen and its likelihood to spread from one host to the other. A sub-field of epidemiology, mathematical epidemiology, uses mathematical tools of different complexity to model and predict the spread of diseases, and to assess the effect of mitigating interventions. Social sciences address the understanding of human behavior: how we think, how we react to external stimuli, and how we interact with other humans. In order to predict the diffusion of real-world diseases, a simplified representation of these concepts has to be proposed. Network science offers a simplified description of how humans interact among themselves. Networks comprise nodes, representing the individuals, and links, modeling pairwise interactions between individuals. Networks only describe the substrate over which an epidemic may spread. Network-based epidemic models must be completed by a dynamic model of the progression of the epidemic at the individual level (i.e., the node dynamics) and a dynamic model of the diffusion over a link (i.e., the dynamics of interactions). Both dynamics are studied on the basis of theories and experimental evidence gathered from epidemiology and social sciences, and can be described by different means, within the two general families of deterministic or stochastic dynamical processes. There are three key ingredients that are needed to model the spread of diseases: a description of human behavior, a representation of human interactions, and a characterization of the progression and diffusion of diseases. These three ingredients, conveniently simplified, have to be combined toward the realization of richer models that could beget a more reliable description of reality. Recent advances have mostly focused on the development of the first two ingredients, while modeling of diseases is a relatively well established domain since decades. In this dissertation, we advance the current state-of-the-art by broadening our understanding of human-to-human interactions and their integration within modeling the diffusion of real diseases. Our first line of research entails the development of new methodologies that account for changes in human behavior over time. Such methodologies are designed to analyze real networks and classify human-to-human interactions into two classes, those generated at random (e.g., acquaintances, casual encounters) and those generated because of a social bound (e.g. friendship, work, family relationship). Random interactions are also called ``weak ties'' or ``reducible links'' because they pertain uniquely to node-specific properties and do not reflect the dyadic relationships between nodes. Interactions generated because of social bounds are also termed ``strong ties'' or ``irreducible links'', as they reflect the dyadic relationships between nodes. Our second line of research includes the study of disease processes unfolding on the networks' fabric. Specifically, we design two new models of networks: one in which behavioral changes, reducible, and irreducible links coexist; and another where realistic mobility patterns are coupled with the presence of a core-peripheral structure, typical of many real cities. Our models are inspired by experimental evidence, and they can be used to improve our current understanding about the diffusion of diseases. Finally, this dissertation has also an educational objective, which involves the design of a mobile application that uses networks to teach the best practices to prevent the spread of flu to the general public. Our mobile application ``StopTheSpread'' is distributed by the New York University and freely available in both the Google Play and Apple Stores
Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size
Full text linkFiltering information in complex networks entails the process of removing interactions explained by a proper null hypothesis and retaining the remaining interactions, which form the backbone network. The reconstructed backbone network depends upon the accuracy and reliability of the available tools, which, in turn, are affected by the specific features of the available dataset. Here, we examine the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties. In addition to the recently proposed evolving activity driven model, we extend two existing approaches (the disparity filter and the temporal fitness model) to tackle time-varying phenomena. Our analysis focuses on the influence of the network size, which was previously shown to be a determining factor for the performance of the evolving activity driven model. Through mathematical and numerical analysis, we propose general guidelines for the use of these three approaches based on the available dataset. For small networks, the evolving temporal fitness model offers a more reasonable trade-off between the number of links assigned to the backbone network and the accuracy of their inference. The main limitation of this methodology lies in its computational cost, which becomes excessively high for large networks. In this case, the evolving activity driven model could be a valid substitute to the evolving temporal fitness model. If one seeks to minimize the number of links inaccurately included in the backbone network at the risk of dismissing many links that could belong to it, then the temporal disparity filter would be the approach-of-choice. Overall, our contribution expands the toolbox of network discovery in the technical literature and should help users in choosing the right network discovery instrument, depending on the problem considered
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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