Journal•ISSN: 1434-6028
European Physical Journal B
Springer Science+Business Media
About: European Physical Journal B is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Phase transition & Superconductivity. It has an ISSN identifier of 1434-6028. Over the lifetime, 14316 publications have been published receiving 288622 citations. The journal is also known as: European Physics Journal B: Condensed Matter and Complex Systems & European Physical Journal B. Condensed Matter and Complex Systems.
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TL;DR: In this paper, Ba−La−Cu−O system, with the composition BaxLa5−xCu5O5(3−y) have been prepared in polycrystalline form, and samples with x=1 and 0.75,y>0, annealed below 900°C under reducing conditions, consist of three phases, one of them a perovskite-like mixed-valent copper compound.
Abstract: Metallic, oxygen-deficient compounds in the Ba−La−Cu−O system, with the composition BaxLa5−xCu5O5(3−y) have been prepared in polycrystalline form. Samples withx=1 and 0.75,y>0, annealed below 900°C under reducing conditions, consist of three phases, one of them a perovskite-like mixed-valent copper compound. Upon cooling, the samples show a linear decrease in resistivity, then an approximately logarithmic increase, interpreted as a beginning of localization. Finally an abrupt decrease by up to three orders of magnitude occurs, reminiscent of the onset of percolative superconductivity. The highest onset temperature is observed in the 30 K range. It is markedly reduced by high current densities. Thus, it results partially from the percolative nature, bute possibly also from 2D superconducting fluctuations of double perovskite layers of one of the phases present.
10,272 citations
TL;DR: A number of more recent algorithms that appear to work well with real-world network data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks are described.
Abstract: There has been considerable recent interest in algorithms for finding communities in networks— groups of vertices within which connections are dense, but between which connections are sparser. Here we review the progress that has been made towards this end. We begin by describing some traditional methods of community detection, such as spectral bisection, the Kernighan-Lin algorithm and hierarchical clustering based on similarity measures. None of these methods, however, is ideal for the types of real-world network data with which current research is concerned, such as Internet and web data and biological and social networks. We describe a number of more recent algorithms that appear to work well with these data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks.
2,032 citations
TL;DR: A hierarchical arrangement of stocks traded in a financial market is found by investigating the daily time series of the logarithm of stock price and the hierarchical tree of the subdominant ultrametric space associated with the graph provides a meaningful economic taxonomy.
Abstract: I find a hierarchical arrangement of stocks traded in a financial market by investigating the daily time series of the logarithm of stock price. The topological space is a subdominant ultrametric space associated with a graph connecting the stocks of the portfolio analyzed. The graph is obtained starting from the matrix of correlation coefficient computed between all pairs of stocks of the portfolio by considering the synchronous time evolution of the difference of the logarithm of daily stock price. The hierarchical tree of the subdominant ultrametric space associated with the graph provides a meaningful economic taxonomy.
1,808 citations
TL;DR: In this paper, the authors examined the distribution of citations for papers published in 1981 in journals which were cataloged by the Institute for Scientific Information (IISI) and 20 years of publications in Physical Review D, vol. 11-50 (24,296 papers).
Abstract: Numerical data for the distribution of citations are examined for: (i) papers published in 1981 in journals which are catalogued by the Institute for Scientific Information (783,339 papers) and (ii) 20 years of publications in Physical Review D, vols. 11-50 (24,296 papers). A Zipf plot of the number of citations to a given paper versus its citation rank appears to be consistent with a power-law dependence for leading rank papers, with exponent close to -1/2. This, in turn, suggests that the number of papers with x citations, N(x), has a large-x power law decay $$N(x) \sim {x^{ - a}}$$
, with $$a \approx 3$$
.
1,476 citations
TL;DR: In this paper, the authors empirically investigate a simple framework of link prediction on the basis of node similarity and propose a new similarity measure, motivated by the resource allocation process taking place on networks, which can remarkably enhance the prediction accuracy.
Abstract: Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.
1,284 citations