  Networks effectively capture interactions among components of complex
systems, and have thus become a mainstay in many scientific disciplines.
Growing evidence, especially from biology, suggest that networks undergo
changes over time, and in response to external stimuli. In biology and
medicine, these changes have been found to be predictive of complex diseases.
They have also been used to gain insight into mechanisms of disease initiation
and progression. Primarily motivated by biological applications, this article
provides a review of recent statistical machine learning methods for inferring
networks and identifying changes in their structures.
