462 research outputs found
Universality of citation distributions: towards an objective measure of scientific impact
We study the distributions of citations received by a single publication
within several disciplines, spanning broad areas of science. We show that the
probability that an article is cited times has large variations between
different disciplines, but all distributions are rescaled on a universal curve
when the relative indicator is considered, where is the
average number of citations per article for the discipline. In addition we show
that the same universal behavior occurs when citation distributions of articles
published in the same field, but in different years, are compared. These
findings provide a strong validation of as an unbiased indicator for
citation performance across disciplines and years. Based on this indicator, we
introduce a generalization of the h-index suitable for comparing scientists
working in different fields.Comment: 7 pages, 5 figures. accepted for publication in Proc. Natl Acad. Sci.
US
Redundant Interdependencies Boost the Robustness of Multiplex Networks
12 pages, 10 figures + Supp MatF. R. acknowledges support from the National Science Foundation (Grant No. CMMI-1552487) and the U.S. Army Research Office (Grant No. W911NF-16-1-0104)
Epidemic plateau in critical susceptible-infected-removed dynamics with nontrivial initial conditions
(11 pages, 10 figures)Containment measures implemented by some countries to suppress the spread of COVID-19 have resulted in a slowdown of the epidemic characterized by time series of daily infections plateauing over extended periods of time. We prove that such a dynamical pattern is compatible with critical Susceptible-Infected-Removed (SIR) dynamics. In traditional analyses of the critical SIR model, the critical dynamical regime is started from a single infected node. The application of containment measures to an ongoing epidemic, however, has the effect to make the system enter in its critical regime with a number of infected individuals potentially large. We describe how such non-trivial starting conditions affect the critical behavior of the SIR model. We perform a theoretical and large-scale numerical investigation of the model. We show that the expected outbreak size is an increasing function of the initial number of infected individuals, while the expected duration of the outbreak is a non-monotonic function of the initial number of infected individuals. Also, we precisely characterize the magnitude of the fluctuations associated with the size and duration of the outbreak in critical SIR dynamics with non-trivial initial conditions. Far from heard immunity, fluctuations are much larger than average values, thus indicating that predictions of plateauing time series may be particularly challenging
A paradox in community detection
Recent research has shown that virtually all algorithms aimed at the
identification of communities in networks are affected by the same main
limitation: the impossibility to detect communities, even when these are
well-defined, if the average value of the difference between internal and
external node degrees does not exceed a strictly positive value, in literature
known as detectability threshold. Here, we counterintuitively show that the
value of this threshold is inversely proportional to the intrinsic quality of
communities: the detection of well-defined modules is thus more difficult than
the identification of ill-defined communities.Comment: 5 pages, 3 figure
An Introduction to Community Detection in Multi-layered Social Network
Social communities extraction and their dynamics are one of the most
important problems in today's social network analysis. During last few years,
many researchers have proposed their own methods for group discovery in social
networks. However, almost none of them have noticed that modern social networks
are much more complex than few years ago. Due to vast amount of different data
about various user activities available in IT systems, it is possible to
distinguish the new class of social networks called multi-layered social
network. For that reason, the new approach to community detection in the
multi-layered social network, which utilizes multi-layered edge clustering
coefficient is proposed in the paper.Comment: M.D. Lytras et al. (Eds.): WSKS 2011, CCIS 278, pp. 185-190, 201
Message-Passing Methods for Complex Contagions
Message-passing methods provide a powerful approach for calculating the
expected size of cascades either on random networks (e.g., drawn from a
configuration-model ensemble or its generalizations) asymptotically as the
number of nodes becomes infinite or on specific finite-size networks. We
review the message-passing approach and show how to derive it for
configuration-model networks using the methods of (Dhar et al., 1997) and
(Gleeson, 2008). Using this approach, we explain for such networks how to
determine an analytical expression for a "cascade condition", which determines
whether a global cascade will occur. We extend this approach to the
message-passing methods for specific finite-size networks (Shrestha and Moore,
2014; Lokhov et al., 2015), and we derive a generalized cascade condition.
Throughout this chapter, we illustrate these ideas using the Watts threshold
model.Comment: 14 pages, 3 figure
Who is the best player ever? A complex network analysis of the history of professional tennis
We consider all matches played by professional tennis players between 1968
and 2010, and, on the basis of this data set, construct a directed and weighted
network of contacts. The resulting graph shows complex features, typical of
many real networked systems studied in literature. We develop a diffusion
algorithm and apply it to the tennis contact network in order to rank
professional players. Jimmy Connors is identified as the best player of the
history of tennis according to our ranking procedure. We perform a complete
analysis by determining the best players on specific playing surfaces as well
as the best ones in each of the years covered by the data set. The results of
our technique are compared to those of two other well established methods. In
general, we observe that our ranking method performs better: it has a higher
predictive power and does not require the arbitrary introduction of external
criteria for the correct assessment of the quality of players. The present work
provides a novel evidence of the utility of tools and methods of network theory
in real applications.Comment: 10 pages, 4 figures, 4 table
Defining and identifying communities in networks
The investigation of community structures in networks is an important issue
in many domains and disciplines. This problem is relevant for social tasks
(objective analysis of relationships on the web), biological inquiries
(functional studies in metabolic, cellular or protein networks) or
technological problems (optimization of large infrastructures). Several types
of algorithm exist for revealing the community structure in networks, but a
general and quantitative definition of community is still lacking, leading to
an intrinsic difficulty in the interpretation of the results of the algorithms
without any additional non-topological information. In this paper we face this
problem by introducing two quantitative definitions of community and by showing
how they are implemented in practice in the existing algorithms. In this way
the algorithms for the identification of the community structure become fully
self-contained. Furthermore, we propose a new local algorithm to detect
communities which outperforms the existing algorithms with respect to the
computational cost, keeping the same level of reliability. The new algorithm is
tested on artificial and real-world graphs. In particular we show the
application of the new algorithm to a network of scientific collaborations,
which, for its size, can not be attacked with the usual methods. This new class
of local algorithms could open the way to applications to large-scale
technological and biological applications.Comment: Revtex, final form, 14 pages, 6 figure
Explosive Percolation in the Human Protein Homology Network
We study the explosive character of the percolation transition in a
real-world network. We show that the emergence of a spanning cluster in the
Human Protein Homology Network (H-PHN) exhibits similar features to an
Achlioptas-type process and is markedly different from regular random
percolation. The underlying mechanism of this transition can be described by
slow-growing clusters that remain isolated until the later stages of the
process, when the addition of a small number of links leads to the rapid
interconnection of these modules into a giant cluster. Our results indicate
that the evolutionary-based process that shapes the topology of the H-PHN
through duplication-divergence events may occur in sudden steps, similarly to
what is seen in first-order phase transitions.Comment: 13 pages, 6 figure
A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions
The large amount of information contained in bibliographic databases has
recently boosted the use of citations, and other indicators based on citation
numbers, as tools for the quantitative assessment of scientific research.
Citations counts are often interpreted as proxies for the scientific influence
of papers, journals, scholars, and institutions. However, a rigorous and
scientifically grounded methodology for a correct use of citation counts is
still missing. In particular, cross-disciplinary comparisons in terms of raw
citation counts systematically favors scientific disciplines with higher
citation and publication rates. Here we perform an exhaustive study of the
citation patterns of millions of papers, and derive a simple transformation of
citation counts able to suppress the disproportionate citation counts among
scientific domains. We find that the transformation is well described by a
power-law function, and that the parameter values of the transformation are
typical features of each scientific discipline. Universal properties of
citation patterns descend therefore from the fact that citation distributions
for papers in a specific field are all part of the same family of univariate
distributions.Comment: 9 pages, 6 figures. Supporting information files available at
http://filrad.homelinux.or
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