scispace - formally typeset
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.

read more

Citations
More filters
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.
Journal ArticleDOI

Statistical physics of social dynamics

TL;DR: In this article, a wide list of topics ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading are reviewed and connections between these problems and other, more traditional, topics of statistical physics are highlighted.
Journal ArticleDOI

Evolution of networks

TL;DR: The recent rapid progress in the statistical physics of evolving networks is reviewed, and how growing networks self-organize into scale-free structures is discussed, and the role of the mechanism of preferential linking is investigated.
Journal ArticleDOI

Epidemic processes in complex networks

TL;DR: A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear.
References
More filters

Unleashing the power of semantic text analysis: a complex systems approach

TL;DR: An informationtheoretic measure is developed, based on the maximum entropy principle, to quantify the information content of scientific concepts and it is proved that the removal of generic concepts is beneficial in terms of the sparsity of the similarity network, thus allowing the detection of communities of articles that are related to more specific themes.
Journal ArticleDOI

Tolerance analysis in scale-free social networks with varying degree exponents

TL;DR: A tolerance analysis is given for the tradeoff and guideline is drawn to help better design of scale-free network for degree exponents in range of (0.5, 2) and (3, 4.5) using network size 1,000, 2,000 and 4,000.
Journal ArticleDOI

Accelerating GPU betweenness centrality

TL;DR: This work presents a hybrid GPU implementation of Betweenness Centrality (BC) that provides good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously, and achieves up to 13× speedup on high-diameter graphs and an average of 2.71× speed up overall compared to the best existing GPU algorithm.
Posted Content

An evolutionary model of long tailed distributions in the social sciences

TL;DR: A parsimonious, stochastic model, which generates an entire family of real-world right-skew socio-economic distributions, including exponential, winner-take-all, power law tails of varying exponents and power laws across the whole data, and produces the continuous turnover observed empirically within those distributions.
Related Papers (5)