  Based on the glocal HIM metric and its induced graph kernel, we propose a
novel solution in differential network analysis that integrates network
comparison and classification tasks. The HIM distance is defined as the
one-parameter family of product metrics linearly combining the normalised
Hamming distance H and the normalised Ipsen-Mikhailov spectral distance IM. The
combination of the two components within a single metric allows overcoming
their drawbacks and obtaining a measure that is simultaneously global and
local. Furthermore, plugging the HIM kernel into a Support Vector Machine gives
us a classification algorithm based on the HIM distance. First, we outline the
theory underlying the metric construction. We introduce two diverse
applications of the HIM distance and the HIM kernel to biological datasets.
This versatility supports the adoption of the HIM family as a general tool for
information extraction, quantifying difference among diverse in- stances of a
complex system. An Open Source implementation of the HIM metrics is provided by
the R package nettols and in its web interface ReNette.
