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## Statistical Network Science with Applications |

Networks have become a
paradigm of science and society. Social developments, such as the rise of the
internet and globalization, have brought the idea of networks to the public's
attention. Many people take part in social networks and the idea of
six-degrees-of-separation is almost taken for granted. Within science, networks
are also starting to play an important role, be it in a more complex way. Attempts
to model complex systems, such as in climate science, system biology and system
chemistry, are very intricate – and, appropriately, require often collaborative
networks of scientists to succeed. Chaotic behaviour
of such mathematically-encoded systems always lurks around the corner. Networks initially
offered a way to simplify complex relationships via a set of pairwise dependence
relationships. More recently, it has become clear that many systems have
non-linear dynamics, so they may have also chaotic behaviour
and strange attractors. In neural systems, new research has shown that chaos
appears on the level of large networks. In population dynamics, chaos has been
observed in experimental cultures and seems to be important in the diffusion of
transmitted diseases in social networks. This workshop focuses on
stochastic networks, whereby the stochasticity can be
an element of the network, or associated with the measurements on the network.
This includes therefore mathematical modelling of
network evolution, such as a description of the Erdös-Rényi
random networks and the countless extensions thereof, but also statistical
physical model for the spread of infectious diseases or the statistical
inference of a signalling pathway on the basis of
time-course protein measurements. Underlying each of these models are ways to
model a network stochastically. It is rare, however, that sociological network modellers interact with biological modellers.
This is unfortunate and this meeting is intended to bring the various
traditions together. [Back] |