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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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Journal ArticleDOI

Inferring mechanisms for sexual partnership formation from the distribution of sexual partner numbers.

TL;DR: Examining in detail the reported distribution of sexual partner numbers from 5 population-based surveys within the United States finds that the data across the different studies cannot reliably distinguish between the different models.

Human Sexual Networks.

TL;DR: The specific point at which an epidemic is possible is referred to as the epidemic threshold, and the basic reproduction rate is usually defined as the average number of secondary infections caused by one infectious individual that enters into a totally susceptible population.
Journal ArticleDOI

Network Anatomy Controlling Abrupt-like Percolation Transition.

TL;DR: An efficient percolation strategy is derived from this new viewpoint of network anatomy, which enables abrupt-like percolations transition through removal of a small amount of cartilage links, which play a crucial role in network connectivity.
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Dynamically rich, yet parameter-sparse models for spatial epidemiology: Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

TL;DR: The most surprising result arising from the “marriage” between epidemiology and physics was that any spreading rate of a disease in a scale-free network, no matter how low it is, causes the infection to spread over the whole network.
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On the Degree Distribution of Faulty Peer-to-Peer Overlay Networks

TL;DR: Results show that based on the provided mathematical model, it is possible to properly tune the average attachment rate at peers so as they are enabled to maintain their own desired degree.
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.
Journal ArticleDOI

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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