The web of human sexual contacts

By Fredrik Liljeros, Christofer R Edling, L. A. Amaral, H. Eugene Stanley, Y. Åberg. Nature, 2001

1,666 citations recorded by the source corpus.

Promiscuous individuals are the vulnerable nodes to target in safe-sex campaigns.

Subfields: Epidemiology, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/108c97fe81903423ed52d46bae5708e5182f951a

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Citations in the corpus

A high-resolution human contact network for infectious disease transmission (2010, background): “Many networks from a wide range of applications, including contact networks relevant for infectious disease transmission (19, 20), have been found to have highly heterogeneous degree distributions, however.”
Graph theory and networks in Biology. (2006, background): “Recently, researchers have begun to investigate how the novel properties observed in social networks and in networks of human sexual contacts [236] effect the behaviour of various models of disease spread.”
The spread of awareness and its impact on epidemic outbreaks (2009, background): “This could be of relevance in the case of sexually transmitted disease, where a strong heterogeneity in the relevant network has been observed (29) and highly sexually active individuals are of crucial importance, yet do not necessarily find themselves in the same parts of the communication network ”
Measuring the Structure of Road Networks (2007, background): “…to as “scale-free networks” in a study of the World Wide Web (Albert et al., 1999) and have been observed in a host of other networks, notably including citation networks (Price, 1965), metabolic networks (Jeong et al., 2000), and the network of human sexual contacts (Liljeros et al., 2001).”
Networks and the Epidemiology of Infectious Disease (2010, background): “2) that drives the infection dynamics [22] although larger-scale structure does play a role [23]; for social interactions, it is the assortativity between (age-) groups that controls the behaviour, with the number of contacts being distributed as a negative binomial [17].”
Mixing patterns in networks. (2002, background): “Suppose for example that we are interested, as many people seem to be these days, in networks that have power-law degree distributions, p k ∼ k − τ [1, 2, 3, 7, 33, 49, 57, 58, 59, 60, 61].”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, methodology): “Direct methods rely on data collection about at-risk events via diaries [11,12] or time-use data [2,27].”
Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. (2007, background): “…et al. , 2004; Barrat et al. , 2004; Guimer´a et al. , 2005; Chowell et al. , 2003) have been analyzed and found to exhibit complex features encoded in large scale heterogeneity, self-organization and other properties typical of complex systems (Albert & Barab´asi, 2002; Dorogovt-sev…”
When individual behaviour matters: homogeneous and network models in epidemiology (2007, background): “…on scale-free random networks (May & Lloyd 2001; Dezso & Barabasi 2002; Pastor-Satorras & Vespignani 2002), based on the apparent ubiquity of such networks in natural and human-made systems and a (limited) set of studies of epidemiologically relevant contact patterns (Liljeros et al. 2001).”
Random Graphs and Complex Networks (2016, background): “Liljeros, Edling, Amaral and Stanley [153] investigated sexual networks in Sweden, where two people are connected when they have had a sexual relation in the previous year, finding that the degree distributions of males and females obey power laws, with estimated exponents of τfem ≈ 2.”