74 research outputs found
Comparing the hierarchy of keywords in on-line news portals
The tagging of on-line content with informative keywords is a widespread
phenomenon from scientific article repositories through blogs to on-line news
portals. In most of the cases, the tags on a given item are free words chosen
by the authors independently. Therefore, relations among keywords in a
collection of news items is unknown. However, in most cases the topics and
concepts described by these keywords are forming a latent hierarchy, with the
more general topics and categories at the top, and more specialised ones at the
bottom. Here we apply a recent, cooccurrence-based tag hierarchy extraction
method to sets of keywords obtained from four different on-line news portals.
The resulting hierarchies show substantial differences not just in the topics
rendered as important (being at the top of the hierarchy) or of less interest
(categorised low in the hierarchy), but also in the underlying network
structure. This reveals discrepancies between the plausible keyword association
frameworks in the studied news portals
Hierarchy measure for complex networks
Nature, technology and society are full of complexity arising from the
intricate web of the interactions among the units of the related systems (e.g.,
proteins, computers, people). Consequently, one of the most successful recent
approaches to capturing the fundamental features of the structure and dynamics
of complex systems has been the investigation of the networks associated with
the above units (nodes) together with their relations (edges). Most complex
systems have an inherently hierarchical organization and, correspondingly, the
networks behind them also exhibit hierarchical features. Indeed, several papers
have been devoted to describing this essential aspect of networks, however,
without resulting in a widely accepted, converging concept concerning the
quantitative characterization of the level of their hierarchy. Here we develop
an approach and propose a quantity (measure) which is simple enough to be
widely applicable, reveals a number of universal features of the organization
of real-world networks and, as we demonstrate, is capable of capturing the
essential features of the structure and the degree of hierarchy in a complex
network. The measure we introduce is based on a generalization of the m-reach
centrality, which we first extend to directed/partially directed graphs. Then,
we define the global reaching centrality (GRC), which is the difference between
the maximum and the average value of the generalized reach centralities over
the network. We investigate the behavior of the GRC considering both a
synthetic model with an adjustable level of hierarchy and real networks.
Results for real networks show that our hierarchy measure is related to the
controllability of the given system. We also propose a visualization procedure
for large complex networks that can be used to obtain an overall qualitative
picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table
Measurement of Ï production in pp collisions at âs = 2.76 TeV
The production of Ï(1S), Ï(2S) and Ï(3S)
mesons decaying into the dimuon final state is studied with
the LHCb detector using a data sample corresponding to an
integrated luminosity of 3.3 pbâ1 collected in protonâproton
collisions at a centre-of-mass energy of âs = 2.76 TeV. The
differential production cross-sections times dimuon branching
fractions are measured as functions of the Ï transverse
momentum and rapidity, over the ranges pT < 15 GeV/c
and 2.0 < y < 4.5. The total cross-sections in this kinematic
region, assuming unpolarised production, are measured to be
Ï (pp â Ï(1S)X) Ă B
Ï(1S)âÎŒ+ÎŒâ
= 1.111 ± 0.043 ± 0.044 nb,
Ï (pp â Ï(2S)X) Ă B
Ï(2S)âÎŒ+ÎŒâ
= 0.264 ± 0.023 ± 0.011 nb,
Ï (pp â Ï(3S)X) Ă B
Ï(3S)âÎŒ+ÎŒâ
= 0.159 ± 0.020 ± 0.007 nb,
where the first uncertainty is statistical and the second systematic
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