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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

Nature of Complex Network of Dengue Epidemic as a Scale-Free Network

TL;DR: The results showed that the network topology exhibits the features of a scale-free network instead of a random network, which is important for the researchers, health officials, and policy makers who deal with arbovirus epidemic diseases.
Journal ArticleDOI

Large-Scale Network Reduction Towards Scale-Free Structure

TL;DR: An algorithm allowing to find an approximate solution to the optimization problem of graph reduction is derived and it is shown that, if the initial network is a flow network, it is possible to design the algorithm such that the output remains aflow network.
Journal ArticleDOI

Probabilistic Inductive Classes of Graphs

TL;DR: Probabilistic inductive classes of graphs (PICGs) as discussed by the authors are defined by imposing a probability space on the rules and their left elements from the standard notion of inductive class of graphs and can model the processes creating real-world social networks, such as spread of knowledge, dynamics of acquaintanceships or sexual contacts, and emergence of clusters.
Book ChapterDOI

A simple model for complex networks with arbitrary degree distribution and clustering

TL;DR: A stochastic model for networks with arbitrary degree distributions and average clustering coefficient is presented and is generalizable to include mixing based on attributes and other complex social structure.
Journal ArticleDOI

Diffusion approach for community discovering within the complex networks: LiveJournal study

TL;DR: It is shown that this Internet-based social network of LiveJournal users has a power-law region in degree distribution with exponent γ=3.45 and the well-interconnected cliques of users can be identified as nodes with equal values of concentration.
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.
Journal ArticleDOI

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.
Journal ArticleDOI

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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