  Network classification has a variety of applications, such as detecting
communities within networks and finding similarities between those representing
different aspects of the real world. However, most existing work in this area
focus on examining static undirected networks without considering directed
edges or temporality. In this paper, we propose a new methodology that utilizes
feature representation for network classification based on the temporal motif
distribution of the network and a null model for comparing against random
graphs. Experimental results show that our method improves accuracy by up
$10\%$ compared to the state-of-the-art embedding method in network
classification, for tasks such as classifying network type, identifying
communities in email exchange network, and identifying users given their
app-switching behaviors.
