  Classifying large scale networks into several categories and distinguishing
them according to their fine structures is of great importance with several
applications in real life. However, most studies of complex networks focus on
properties of a single network but seldom on classification, clustering, and
comparison between different networks, in which the network is treated as a
whole. Due to the non-Euclidean properties of the data, conventional methods
can hardly be applied on networks directly. In this paper, we propose a novel
framework of complex network classifier (CNC) by integrating network embedding
and convolutional neural network to tackle the problem of network
classification. By training the classifiers on synthetic complex network data
and real international trade network data, we show CNC can not only classify
networks in a high accuracy and robustness, it can also extract the features of
the networks automatically.
