1,721,013 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Faisabilité des interactions et inférence des réseaux sociaux en ligne
No full textCette thèse traite du problème de l'inférence de réseau dans le domaine des réseaux sociaux en ligne. L'hypothèse principale des problèmes d'inférence de réseau est que le réseau que nous observons n'est pas celui dont nous avons réellement be-soin. Cela est particulièrement vrai dans l'espace numérique actuel, où l'abondance d'informations s'accompagne généralement d'un manque crucial de fiabilité, sous la forme de bruit et de points manquants dans les données. Cependant, les approches existantes ignorent ou ne garantissent pas l'inférence de réseaux d'une manière qui puisse expliquer les données dont nous disposons. Il en résulte une ambiguïté sur la signification du réseau inféré, en plus d'un manque d'intuition et de contrôle sur l'inférence elle-même. L'objectif de cette thèse est d'explorer plus avant ce problème. Pour quantifier la capacité d'un réseau inféré à expliquer un ensemble de données, nous introduisons un nouveau critère de qualité, appelé feasibility. Notre intuition est que si un ensemble de données est "feasible" en ce qui concerne le réseau inféré, celui-ci est un meilleur candidat que le cas échéant. Pour le vérifier, nous proposons une nouvelle méthode d'inférence de réseau sous la forme d'un problème d'optimisation contraint, basé sur le maximum de vraisemblance, qui garantit la feasibility à 100%. Cette méthode est adaptée aux données provenant des réseaux sociaux en ligne, qui sont des sources bien connues de données peu fiables et restreintes. Nous présentons des expériences sur un ensemble de données synthétiques et données réelles provenant de la plateforme Twitter/X. Nous montrons que la méthode proposée génère une distribution a posteriori des graphes qui garantit l'explication de l'ensemble de données tout en étant plus proche de la véritable structure sous-jacente. En guise d'exploration finale, nous nous penchons sur le domaine de l'apprentissage profond pour trouver des alternatives plus évolutives et plus flexibles, en fournissant un cadre préliminaire basé sur les réseaux neuronaux graphiques et l'apprentissage contrastif qui donne des résultats prometteurs.This thesis deals with the problem of network inference in the domain of Online So-cial Networks. The main premise of network inference problems is that the networkwe are observing is not the network that we really need. This is especially prevalentin today's digital space, where the abundance of information usually comes withcrucial unreliability, in the form of noise and missing points in the data. However, existing approaches either ignore or do not guarantee to infer networks in a waythat can explain the data we have at hand. As a result, there is an ambiguity around the meaning of the network that we are inferring, while also having little intuition or control over the inference itself. The goal of this thesis is to further explore this problem. To quantify how well an inferred network can explain a dataset, we introduce a novel quality criterion called feasibility. Our intuition is that if a dataset is feasible given an inferred network, we might also be closer to the ground truth. To verify this,we propose a novel network inference method in the form of a constrained, Maximum Likelihood-based optimization problem that guarantees 100% feasibility. It is tailored to inputs from Online Social Networks, which are well-known sources of un-reliable and restricted data. We provide extensive experiments on one synthetic andone real-world dataset coming from Twitter/X. We show that our proposed method generates a posterior distribution of graphs that guarantees to explain the dataset while also being closer to the true underlying structure when compared to other methods. As a final exploration, we look into the field of deep learning for more scalable and flexible alternatives, providing a preliminary framework based on Graph Neural Networks and contrastive learning that gives promising results
Variations on the Author
Get PDF“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
Get PDFWe 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
Representation Learning for Time-Series Forecasting and Classification
No full textNous nous intéressons au développement de méthodes qui répondent aux difficultés posées par l’analyse des séries temporelles. Nos contributions se focalisent sur deux tâches : la prédiction de séries temporelles et la classification de séries temporelles. Notre première contribution présente une méthode de prédiction et de complétion de séries temporelles multivariées et relationnelles. Le but est d’être capable de prédire simultanément l’évolution d’un ensemble de séries temporelles reliées entre elles selon un graphe, ainsi que de compléter les valeurs manquantes dans ces séries (pouvant correspondre par exemple à une panne d’un capteur pendant un intervalle de temps donné). On se propose d’utiliser des techniques d’apprentissage de représentation pour prédire l’évolution des séries considérées tout en complétant les valeurs manquantes et prenant en compte les relations qu’il peut exister entre elles. Des extensions de ce modèle sont proposées et décrites : d’abord dans le cadre de la prédiction de séries temporelles hétérogènes puis dans le cas de la prédiction de séries temporelles avec une incertitude exprimée. Un modèle de prédiction de séries spatio-temporelles est ensuiteproposé, avec lequel les relations entre les différentes séries peuvent être exprimées de manière plus générale, et où ces dernières peuvent être apprises.Enfin, nous nous intéressons à la classification de séries temporelles. Un modèle d’apprentissage joint de métrique et de classification de séries est proposé et une comparaison expérimentale est menée.This thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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