  We introduce the \emph{temporal graphlet kernel} for classifying
dissemination processes in labeled temporal graphs. Such dissemination
processes can be spreading (fake) news, infectious diseases, or computer
viruses in dynamic networks. The networks are modeled as labeled temporal
graphs, in which the edges exist at specific points in time, and node labels
change over time. The classification problem asks to discriminate dissemination
processes of different origins or parameters, e.g., infectious diseases with
different infection probabilities. Our new kernel represents labeled temporal
graphs in the feature space of temporal graphlets, i.e., small subgraphs
distinguished by their structure, time-dependent node labels, and chronological
order of edges. We introduce variants of our kernel based on classes of
graphlets that are efficiently countable. For the case of temporal wedges, we
propose a highly efficient approximative kernel with low error in expectation.
We show that our kernels are faster to compute and provide better accuracy than
state-of-the-art methods.
