  This paper deals with the issue of concept drift in supervised machine
learn-ing. We make use of graphical models to elicit the visible structure of
the dataand we infer from there changes in the hidden context. Differently from
previous concept-drift detection methods, this application does not depend on
the supervised machine learning model in use for a specific target variable,
but it tries to assess the concept drift as independent characteristic of the
evolution of a dataset. Specifically, we investigate how a graphical model
evolves by looking at the creation of new links and the disappearing of
existing ones in different time periods. The paper suggests a method that
highlights the changes and eventually produce a metric to evaluate the
stability over time. The paper evaluate the method with real world data on the
Australian Electric market.
