169 research outputs found
Information dynamics shape the networks of Internet-mediated prostitution
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
The influence of risk perception in epidemics: a cellular agent model
Our work stems from the consideration that the spreading of a disease is
modulated by the individual's perception of the infected neighborhood and
his/her strategy to avoid being infected as well. We introduced a general
``cellular agent'' model that accounts for a hetereogeneous and variable
network of connections. The probability of infection is assumed to depend on
the perception that an individual has about the spreading of the disease in her
local neighborhood and on broadcasting media. In the one-dimensional
homogeneous case the model reduces to the DK one, while for long-range coupling
the dynamics exhibits large fluctuations that may lead to the complete
extinction of the disease
Two-level relationships and Scale-Free Networks
Through the distinction between ``real'' and ``virtual'' links between the
nodes of a graph, we develop a set of simple rules leading to scale-free
networks with a tunable degree distribution exponent. Albeit sharing some
similarities with preferential attachment, our procedure is both faster than a
na\"ive implementation of the Barab\'asi and Albert model and exhibits
different clustering properties. The model is thoroughly studied numerically
and suggests that reducing the set of partners a node can connect to is
important in seizing the diversity of scale-free structures
Visual Mining of Epidemic Networks
We show how an interactive graph visualization method based on maximal
modularity clustering can be used to explore a large epidemic network. The
visual representation is used to display statistical tests results that expose
the relations between the propagation of HIV in a sexual contact network and
the sexual orientation of the patients.Comment: 8 page
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How people interact in evolving online affiliation networks
The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks
A weighted configuration model and inhomogeneous epidemics
A random graph model with prescribed degree distribution and degree dependent
edge weights is introduced. Each vertex is independently equipped with a random
number of half-edges and each half-edge is assigned an integer valued weight
according to a distribution that is allowed to depend on the degree of its
vertex. Half-edges with the same weight are then paired randomly to create
edges. An expression for the threshold for the appearance of a giant component
in the resulting graph is derived using results on multi-type branching
processes. The same technique also gives an expression for the basic
reproduction number for an epidemic on the graph where the probability that a
certain edge is used for transmission is a function of the edge weight. It is
demonstrated that, if vertices with large degree tend to have large (small)
weights on their edges and if the transmission probability increases with the
edge weight, then it is easier (harder) for the epidemic to take off compared
to a randomized epidemic with the same degree and weight distribution. A recipe
for calculating the probability of a large outbreak in the epidemic and the
size of such an outbreak is also given. Finally, the model is fitted to three
empirical weighted networks of importance for the spread of contagious diseases
and it is shown that can be substantially over- or underestimated if the
correlation between degree and weight is not taken into account
Analytical solution of a model for complex food webs
We investigate numerically and analytically a recently proposed model for
food webs [Nature {\bf 404}, 180 (2000)] in the limit of large web sizes and
sparse interaction matrices. We obtain analytical expressions for several
quantities with ecological interest, in particular the probability
distributions for the number of prey and the number of predators. We find that
these distributions have fast-decaying exponential and Gaussian tails,
respectively. We also find that our analytical expressions are robust to
changes in the details of the model.Comment: 4 pages (RevTeX). Final versio
Worldwide spreading of economic crisis
We model the spreading of a crisis by constructing a global economic network
and applying the Susceptible-Infected-Recovered (SIR) epidemic model with a
variable probability of infection. The probability of infection depends on the
strength of economic relations between the pair of countries, and the strength
of the target country. It is expected that a crisis which originates in a large
country, such as the USA, has the potential to spread globally, like the recent
crisis. Surprisingly we show that also countries with much lower GDP, such as
Belgium, are able to initiate a global crisis. Using the {\it k}-shell
decomposition method to quantify the spreading power (of a node), we obtain a
measure of ``centrality'' as a spreader of each country in the economic
network. We thus rank the different countries according to the shell they
belong to, and find the 12 most central countries. These countries are the most
likely to spread a crisis globally. Of these 12 only six are large economies,
while the other six are medium/small ones, a result that could not have been
otherwise anticipated. Furthermore, we use our model to predict the crisis
spreading potential of countries belonging to different shells according to the
crisis magnitude.Comment: 13 pages, 4 figures and Supplementary Materia
Random Sierpinski network with scale-free small-world and modular structure
In this paper, we define a stochastic Sierpinski gasket, on the basis of
which we construct a network called random Sierpinski network (RSN). We
investigate analytically or numerically the statistical characteristics of RSN.
The obtained results reveal that the properties of RSN is particularly rich, it
is simultaneously scale-free, small-world, uncorrelated, modular, and maximal
planar. All obtained analytical predictions are successfully contrasted with
extensive numerical simulations. Our network representation method could be
applied to study the complexity of some real systems in biological and
information fields.Comment: 7 pages, 9 figures; final version accepted for publication in EPJ
Epidemic dynamics in finite size scale-free networks
Many real networks present a bounded scale-free behavior with a connectivity
cut-off due to physical constraints or a finite network size. We study epidemic
dynamics in bounded scale-free networks with soft and hard connectivity
cut-offs. The finite size effects introduced by the cut-off induce an epidemic
threshold that approaches zero at increasing sizes. The induced epidemic
threshold is very small even at a relatively small cut-off, showing that the
neglection of connectivity fluctuations in bounded scale-free networks leads to
a strong over-estimation of the epidemic threshold. We provide the expression
for the infection prevalence and discuss its finite size corrections. The
present work shows that the highly heterogeneous nature of scale-free networks
does not allow the use of homogeneous approximations even for systems of a
relatively small number of nodes.Comment: 4 pages, 2 eps figure
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