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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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Evolutionary analysis of the collaboration networks within National Quality Award Projects of China

TL;DR: Wang et al. as discussed by the authors constructed the inter-contractors' collaboration networks by mapping the electronic database of NQAPC for an 8-year period (2003-2010), in which nodes represent contractors; two contractors are connected by an edge if they have cooperated at least one project.
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The impact of contact structure on infectious disease control: influenza and antiviral agents

TL;DR: Simulation of contact structure in networks with variable degree distributions implies that the various contact networks' degree distributions as well as the allocation of contagiousness between close and casual contacts should be examined to identify appropriate strategies of disease control measures.
Journal ArticleDOI

Fractal and multifractal analyses of bipartite networks.

TL;DR: The results indicate that the multifractality exists in those bipartite networks possessing fractality, and modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartites networks and their corresponding node- Weighted versions.
Posted Content

Effects of Weak Ties on Epidemic Predictability in Community Networks

TL;DR: The effects of the degree of bridge nodes on the variabilities of both the arrival time and the prevalence of disease are investigated, and it is found that the bridge node with a small degree can enhance the predictability of epidemic spreading.
Journal ArticleDOI

Structure of sexual networks determines the operation of sexual selection.

TL;DR: It is shown that mating assortment is highly variable in nature and simulations reveal that such variation plays a key—but so far unappreciated—role in determining the strength of sexual selection on males, and that jointly considering sexual network structure and average polyandry more precisely describes the strengthof sexual selection.
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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