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Open AccessJournal ArticleDOI

The Web of Human Sexual Contacts

TLDR
In this article, the authors analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents for females and males.
Abstract
Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

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Citations
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Topological properties of a time-integrated activity-driven network.

TL;DR: The analytical approach, which highlights the differences of the model with respect to the empirical observations made in real social networks, can be easily extended to deal with improved, more realistic modifications of the activity-driven network paradigm.
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Network science: a useful tool in economics and finance

TL;DR: In this paper, the authors review several applications of network science in finance and economics, and discuss existing challenges and future directions which will substantiate network science as a key tool for financial academics, practitioners, and policy and decision makers.
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On fitting the Pareto–Levy distribution to stock market index data: Selecting a suitable cutoff value

TL;DR: In this article, a more objective procedure based on a statistical measure of discrepancy between the empirical and the Pareto-Levy distribution is presented for data sets from the New York Stock Exchange (DJIA) and the Mexican Stock Market (IPC).
Journal IssueDOI

Exploiting graphical processing units for data-parallel scientific applications

TL;DR: A surprising variation in the performance that can be achieved on GPUs for applications is found and how these findings relate to past known effects in parallel computing such as memory speed-related super-linear speed up is discussed.
References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Emergence of Scaling in Random Networks

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
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The Structure and Function of Complex Networks

Mark Newman
- 01 Jan 2003 - 
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI

Complex networks: Structure and dynamics

TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
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