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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

Stochastic opinion formation in scale-free networks.

TL;DR: The dynamics of opinion formation in large groups of people is mimicked as a stochastic response of each agent to the opinion of his/her neighbors in the social network and to feedback from the average opinion of the whole.
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Influence of contact heterogeneity on TB reproduction ratio R0 in a free-living brushtail possum Trichosurus vulpecula population.

TL;DR: The relevance of refining epidemiological models used to inform disease management policy to account for contact heterogeneity is argued, with implications for the management of TB in New Zealand where the possum is the principal wildlife reservoir host of Mycobacterium bovis, the causal agent of bovine TB.
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Efficient network disintegration under incomplete information: the comic effect of link prediction

TL;DR: By using link prediction method to recover partial missing links in advance, the method can largely improve the network disintegration performance and even outperforms than the results based on complete information when the size of missing information is relatively small.
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Effect of coagulation of nodes in an evolving complex network.

TL;DR: A new type of stochastic network evolution model based on annihilation, creation, and coagulation of nodes, together with the preferential attachment rule is proposed that is consistent with the empirical results of a business transaction network having about 1×10(6) firms.
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Model for generating artificial social networks having community structures with small-world and scale-free properties

TL;DR: A new network generation model is defined which exhibits all the fundamental properties of complex networks along with the presence of community structures and is shown to be different from having high clustering coefficient.
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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