1,720,971 research outputs found
Spreading dynamics in heterogeneous graphs: Beyond the assortativity coefficient
No full textWe study spreading dynamics of a reaction–diffusion process in a special class of heterogeneous graphs with Poissonian degree distribution and composed of both local and long range links. The behavior of the spreading dynamics on such networks are investigated by relating them to the topological features of graphs. We find that the degree of assortativity can give just some indication about the large scale behavior of the spreading dynamics while a detailed description of the process can be addressed by introducing new, more appropriate, topological quantities linked to the distance between nodes
A parametric study of the term structure dynamics
No full textWe present an analysis of the dynamics of the term structure of interest rates based on the study of the time evolution of the parameters of a variation of the Nelson–Siegel model. The results show that it is extremely difficult to find a relation between the evolution of the term structure and the behavior of macroeconomic variables different from the official interest rate
Robust estimation of time-dependent precision matrix with application to the cryptocurrency market
Full text linkMost financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the “maximization–minimization” approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature
Linear and anomalous front propagation in systems with non-Gaussian diffusion: The importance of tails
No full textWe investigate front propagation in systems with diffusive and subdiffusive behavior. The scaling behavior ofmoments of the diffusive problem, both in the standard and in the anomalous cases, is not enough to determine thefeatures of the reactive front. In fact, the shape of the bulk of the probability distribution of the transport process,which determines the diffusive properties, is important just for preasymptotic behavior of front propagation, whilethe precise shape of the tails of the probability distribution determines asymptotic behavior of front propagatio
Reaction Spreading in Systems with Anomalous Diffusion
No full textWe briefly review some aspects of the anomalous diffusion, and itsrelevance in reactive systems. In particular we considerstrong anoma-lousdiffusion characterized by the moment behaviour〈x(t)q〉∼tqν(q),whereν(q) is a non constant function, and we discuss its consequences.Even in the apparently simple caseν(2) = 1/2, strong anomalous dif-fusion may correspond to non trivial features, such as non Gaussianprobability distribution and peculiar scaling of large order moments.When a reactive term is added to a normal diffusion process, onehas a propagating front with a constant velocity. The presence ofanomalous diffusion by itself does not guarantee a changing inthefront propagation scenario; a key factor to select linear intime orfaster front propagation has been identified in the shape of the prob-ability distribution tail in absence of reaction. In addition, we discussthe reaction spreading on graphs, underlying the major roleof theconnectivity properties of these structures, characterized by thecon-nectivity dimension.
NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification
Full text linkGene-disease associations are fundamental for understanding disease etiology
and developing effective interventions and treatments. Identifying genes not
yet associated with a disease due to a lack of studies is a challenging task in
which prioritization based on prior knowledge is an important element. The
computational search for new candidate disease genes may be eased by
positive-unlabeled learning, the machine learning setting in which only a
subset of instances are labeled as positive while the rest of the data set is
unlabeled. In this work, we propose a set of effective network-based features
to be used in a novel Markov diffusion-based multi-class labeling strategy for
putative disease gene discovery. The performances of the new labeling algorithm
and the effectiveness of the proposed features have been tested on ten
different disease data sets using three machine learning algorithms. The new
features have been compared against classical topological and
functional/ontological features and a set of network- and biological-derived
features already used in gene discovery tasks. The predictive power of the
integrated methodology in searching for new disease genes has been found to be
competitive against state-of-the-art algorithms.Comment: This article has been accepted for publication in Bioinformatics,
Published by Oxford University Pres
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
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
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