  The increasing capacity of high-throughput genomic technologies for
generating time-course data has stimulated a rich debate on the most
appropriate methods to highlight crucial aspects of data structure. In this
work, we address the problem of sparse co-expression network representation of
several time-course stress responses in {\it Saccharomyces cerevisiae}. We
quantify the information preserved from the original datasets under a
graph-theoretical framework and evaluate how cross-stress features can be
identified. This is performed both from a node and a network community
organization point of view. Cluster analysis, here viewed as a problem of
network partitioning, is achieved under state-of-the-art algorithms relying on
the properties of stochastic processes on the constructed graphs. Relative
performance with respect to a metric-free Bayesian clustering analysis is
evaluated and possible extensions are discussed. We further cluster the
stress-induced co-expression networks generated independently by using their
community organization at multiple scales. This type of protocol allows for an
integration of multiple datasets that may not be immediately comparable, either
due to diverse experimental variations or because they represent different
types of information about the same genes.
