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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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The network of collaboration among rappers and its community structure

TL;DR: The social network formed by the collaboration between rappers is studied using standard statistical techniques for analysing complex networks and a new method that uses weighted edges to determine which connections are most important and revealing among all the communities is analysed.
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Imperfect targeted immunization in scale-free networks

TL;DR: Analysis and simulation results show that there exists a linear relationship between the inverse of the epidemic threshold and the effectiveness of targeted immunization, and even a relatively weak protection over the hub nodes significantly decreases the number of network nodes ever getting infected and therefore enhances network robustness against virus.
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Simulating devil facial tumour disease outbreaks across empirically derived contact networks

TL;DR: The approach provides a template for using empirically obtained data on contact networks to develop models to explore the extent to which network structure influences the probability of extinction and the mean time until extinction, and suggests that non-random network structure may have major implications for the management of wildlife diseases.
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Extremum statistics in scale-free network models.

TL;DR: It is shown by means of extensive simulations that the exponential truncation of the degree distribution governs the scaling of the extreme values and that the distribution of maxima follows the Gumbel statistics.
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Preventives Versus Treatments

TL;DR: In this article, the authors show that the ratio of preventive to treatment producer surplus can be arbitrarily small, in particular when the distribution of consumer values has a Zipf shape and the disease is rare, and they find that proxies for the Zipf-similarity of the disease risk distribution are associated with a significantly lower likelihood of vaccine development but not drug development.
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.
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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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