  Network theory provides novel concepts that promise an improved
characterization of interacting dynamical systems. Within this framework,
evolving networks can be considered as being composed of nodes, representing
systems, and of time-varying edges, representing interactions between these
systems. This approach is highly attractive to further our understanding of the
physiological and pathophysiological dynamics in human brain networks. Indeed,
there is growing evidence that the epileptic process can be regarded as a
large-scale network phenomenon. We here review methodologies for inferring
networks from empirical time series and for a characterization of these
evolving networks. We summarize recent findings derived from studies that
investigate human epileptic brain networks evolving on timescales ranging from
few seconds to weeks. We point to possible pitfalls and open issues, and
discuss future perspectives.
