25,170 research outputs found

    Rich-club vs rich-multipolarization phenomena in weighted networks

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    Large scale hierarchies characterize complex networks in different domains. Elements at their top, usually the most central or influential, may show multipolarization or tend to club forming tightly interconnected communities. The rich-club phenomenon quantified this tendency based on unweighted network representations. Here, we define this metric for weighted networks and discuss the appropriate normalization which preserves nodes' strengths and discounts structural strength-strength correlations if present. We find that in some real networks the results given by the weighted rich-club coefficient can be in sharp contrast to the ones in the unweighted approach. We also discuss that the scanning of the weighted subgraphs formed by the high-strength hubs is able to unveil features contrary to the average: the formation of local alliances in rich-multipolarized environments, or a lack of cohesion even in the presence of rich-club ordering. Beyond structure, this analysis matters for understanding correctly functionalities and dynamical processes relying on hub interconnectedness.Comment: 12 pages, 2 figure

    Weighted Configuration Model

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    The configuration model is one of the most successful models for generating uncorrelated random networks. We analyze its behavior when the expected degree sequence follows a power law with exponent smaller than two. In this situation, the resulting network can be viewed as a weighted network with non trivial correlations between strength and degree. Our results are tested against large scale numerical simulations, finding excellent agreement.Comment: Proceedings CNET200

    Statistical properties and economic implications of Jump-Diffusion Processes with Shot-Noise effects

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    This paper analyzes the Shot-Noise Jump-Diffusion model of Altmann, Schmidt and Stute (2008), which introduces a new situation where the effects of the arrival of rare, shocking information to the financial markets may fade away in the long run. We analyze several economic implications of the model, providing an analytical expression for the process distribution. We also prove that certain specifications of this model can provide negative serial persistence. Additionally, we find that the degree of serial autocorrelation is related to the arrival and magnitude of abnormal information. Finally, a GMM framework is proposed to estimate the model parameters

    A distributional checklist of the leaf-cutting bees (Hymenoptera: Megachilidae) of Florida

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    The leaf-cutting bees are a diverse group which is commonly encountered all across the country. With over 600 North American species, most of which are in the genera Anthidium, Dianthidium, Stelis, Heriades, Hoplitis, Osmia, Megachile and Coelioxys, there are numerous “non-Apis” pollinators. Being an important taxon, there is a definite need for an awareness of their distribution across the United States and, perhaps in time, everywhere. This may allow us to follow the establishment of exotic species such as Megachile [Callomegachile] torrida Smith, M. [Pseudomegachile] lanata Fabricius, and an unidentified exotic which is listed below in the subgenus Callomegachile
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