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
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Alwis Nazir,Alwis Nazir,Takashi Ichinomiya,Nobuteru Miyamura,Yasuaki Sekiya,Yasutomi Kinosada +5 more
TL;DR: The network analysis appears to be a promising method for identifying signals of suicide by analyzing data obtained from the US Food and Drug Administration Adverse Event Reporting System of selective serotonin reuptake inhibitors.
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Phase diagram of a continuous opinion dynamics on Barabasi─Albert networks
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How Behaviour and the Environment Influence Transmission in Mobile Groups
TL;DR: An agent-based model is used to study how the movement of individuals and characteristics of the signal being transmitted modulate transmission and offers a means by which multi-agent systems can be engineered to achieve desired transmission capabilities.
Posted Content
SIR dynamics in structured populations with heterogeneous connectivity
TL;DR: A system of nonlinear differential equations is presented which describes the behavior of epidemics spreading through random networks with arbitrary degree distributions, and the degree distribution is observed to have significant impact on both the final size and time scale of epidemic.