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Like many other social phenomena, prostitution is increasingly coordinated over the Internet. The online behavior affects the offline activity; the reverse is also true. We investigated the reported sexual contacts between 6,624 anonymous escorts and 10,106 sex buyers extracted from an online community from its beginning and six years on. These sexual encounters were also graded and categorized (in terms of the type of sexual activities performed) by the buyers. From the temporal, bipartite network of posts, we found a full feedback loop in which high grades on previous posts affect the future commercial success of the sex worker, and vice versa. We also found a peculiar growth pattern in which the turnover of community members and sex workers causes a sublinear preferential attachment. There is, moreover, a strong geographic influence on network structure--the network is geographically clustered but still close to connected, the contacts consistent with the inverse-square law observed in trading patterns. We also found that the number of sellers scales sublinearly with city size, so this type of prostitution does not, comparatively speaking, benefit much from an increasing concentration of people. complex networks | network dynamics | network topology doi/10.1073/pnas.0914080107

Even though people in our contemporary technological society are depending on communication, our understanding of the underlying laws of human communicational behavior continues to be poorly understood. Here we investigate the communication patterns in 2 social Internet communities in search of statistical laws in human interaction activity. This research reveals that human communication networks dynamically follow scaling laws that may also explain the observed trends in economic growth. Specifically, we identify a generalized version of Gibrat's law of social activity expressed as a scaling law between the fluctuations in the number of messages sent by members and their level of activity. Gibrat's law has been essential in understanding economic growth patterns, yet without an underlying general principle for its origin. We attribute this scaling law to long-term correlation patterns in human activity, which surprisingly span from days to the entire period of the available data of more than 1 year. Further, we provide a mathematical framework that relates the generalized version of Gibrat's law to the long-term correlated dynamics, which suggests that the same underlying mechanism could be the source of Gibrat's law in economics, ranging from large firms, research and development expenditures, gross domestic product of countries, to city population growth. These findings are also of importance for designing communication networks and for the understanding of the dynamics of social systems in which communication plays a role, such as economic markets and political systems. growth | Gibrat's law | long-term correlations | memory | network growth

Many social networks are characterized by a highly uneven distribution of links. The observed skewed distributions have in several cases been attributed to preferential attachment (PA), a tendency among nodes in a growing network to form new links preferentially to nodes with high numbers of links. We test the PA conjecture in sexual contact networks. A maximum likelihood estimation-based expectation-maximization fitting technique is used to model new partners over a 1-year period based on the number of partners in foregoing periods of 2 years, 4 years, and lifetime. The PA model is modified to account for individual heterogeneity in the inclination to find new partners and fitted to Norwegian survey data on heterosexual men and women. Results show evidence of nonrandom, sublinear PA when comparing the growth in 3- to 5-year periods. The potential implications of these findings are discussed. sexual behavior | sexually transmitted diseases

Author(s): Fredrik Liljeros (corresponding author) [1]; Christofer R. Edling [1]; Luís A. Nunes Amaral [2]; H. Eugene Stanley [2]; Yvonne Ãberg [1] Unlike clearly defined 'real-world' networks [1], social networks [...]

Author(s): Fredrik Liljeros (corresponding author) [1, 2]; Christofer R. Edling [2]; H. Eugene Stanley [3]; Y. Ãberg [2]; Luis A. N. Amaral [4] Liljeros et al . reply Jones and [...]