  Networks are fundamental to the study of complex systems, ranging from social
contacts, message transactions, to biological regulations and economical
networks. In many realistic applications, these networks may vary over time.
Modeling and analyzing such temporal properties is of additional interest as it
can provide a richer characterization of relations between nodes in networks.
In this paper, we explore the role of \emph{graphlets} in network
classification for both static and temporal networks. Graphlets are small
non-isomorphic induced subgraphs representing connected patterns in a network
and their frequency can be used to assess network structures. We show that
graphlet features, which are not captured by state-of-the-art methods, play a
significant role in enhancing the performance of network classification. To
that end, we propose two novel graphlet-based techniques, \emph{gl2vec} for
network embedding, and \emph{gl-DCNN} for diffusion-convolutional neural
networks. We demonstrate the efficacy and usability of \emph{gl2vec} and
\emph{gl-DCNN} through extensive experiments using several real-world static
and temporal networks. We find that features learned from graphlets can bring
notable performance increases to state-of-the-art methods in network analysis.
