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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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Traffic-driven epidemic spreading in finite-size scale-free networks.

TL;DR: This work considers the scenario in which epidemic pathways are defined and driven by flows, and shows that the value of the epidemic threshold in scale-free networks depends directly on flow conditions, in particular on the first and second moments of the betweenness distribution given a routing protocol.
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Parallel graph component labelling with GPUs and CUDA

TL;DR: It is shown how different algorithmic variations can be used to best effect depending upon the cluster structure of the graph being labelled and how features of the GPU architectures and host CPUs can be combined tobest effect into a cluster component labelling algorithm for use in high performance simulations.
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Modeling Infection Transmission

TL;DR: A community of epidemiologist modelers is needed for effective robustness assessment of inferences about infection transmission made using stratified rate or risk comparisons, logistic regression models, or proportionate hazards models that assume an absence of transmission.
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Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK

TL;DR: It is argued that, under appropriate conditions, a static network analysis can be an appropriate tool for gaining insights into disease dynamics even when the relevant time-scales are similar, as with FMD.
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Identifying the starting point of a spreading process in complex networks.

TL;DR: This paper analyzes three spreading schemes and proposes and validate an effective methodology for the identification of the source nodes based on the calculation of the centrality of the nodes on the sampled network, expressed here by degree, betweenness, closeness, and eigenvector centrality.
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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