  A main challenge in mining network-based data is finding effective ways to
represent or encode graph structures so that it can be efficiently exploited by
machine learning algorithms. Several methods have focused in network
representation at node/edge or substructure level. However, many real life
challenges such as time-varying, multilayer, chemical compounds and brain
networks involve analysis of a family of graphs instead of single one opening
additional challenges in graph comparison and representation. Traditional
approaches for learning representations relies on hand-crafting specialized
heuristics to extract meaningful information about the graphs, e.g statistical
properties, structural features, etc. as well as engineered graph distances to
quantify dissimilarity between networks. In this work we provide an
unsupervised approach to learn embedding representation for a collection of
graphs so that it can be used in numerous graph mining tasks. By using an
unsupervised neural network approach on input graphs, we aim to capture the
underlying distribution of the data in order to discriminate between different
class of networks. Our method is assessed empirically on synthetic and real
life datasets and evaluated in three different tasks: graph clustering,
visualization and classification. Results reveal that our method outperforms
well known graph distances and graph-kernels in clustering and classification
tasks, being highly efficient in runtime.
