  Graph comparison plays a major role in many network applications. We often
need a similarity metric for comparing networks according to their structural
properties. Various network features - such as degree distribution and
clustering coefficient - provide measurements for comparing networks from
different points of view, but a global and integrated distance metric is still
missing. In this paper, we employ distance metric learning algorithms in order
to construct an integrated distance metric for comparing structural properties
of complex networks. According to natural witnesses of network similarities
(such as network categories) the distance metric is learned by the means of a
dataset of some labeled real networks. For evaluating our proposed method which
is called NetDistance, we applied it as the distance metric in
K-nearest-neighbors classification. Empirical results show that NetDistance
outperforms previous methods, at least 20 percent, with respect to precision.
