2,005 research outputs found
Exploring the assortativity-clustering space of a network's degree sequence
Nowadays there is a multitude of measures designed to capture different
aspects of network structure. To be able to say if the structure of certain
network is expected or not, one needs a reference model (null model). One
frequently used null model is the ensemble of graphs with the same set of
degrees as the original network. In this paper we argue that this ensemble can
be more than just a null model -- it also carries information about the
original network and factors that affect its evolution. By mapping out this
ensemble in the space of some low-level network structure -- in our case those
measured by the assortativity and clustering coefficients -- one can for
example study how close to the valid region of the parameter space the observed
networks are. Such analysis suggests which quantities are actively optimized
during the evolution of the network. We use four very different biological
networks to exemplify our method. Among other things, we find that high
clustering might be a force in the evolution of protein interaction networks.
We also find that all four networks are conspicuously robust to both random
errors and targeted attacks
A network-based threshold model for the spreading of fads in society and markets
We investigate the behavior of a threshold model for the spreading of fads
and similar phenomena in society. The model is giving the fad dynamics and is
intended to be confined to an underlying network structure. We investigate the
whole parameter space of the fad dynamics on three types of network models. The
dynamics we discover is rich and highly dependent on the underlying network
structure. For some range of the parameter space, for all types of substrate
networks, there are a great variety of sizes and life-lengths of the fads --
what one see in real-world social and economical systems
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
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