  Many real-world graphs or networks are temporal, e.g., in a social network
persons only interact at specific points in time. This information directs
dissemination processes on the network, such as the spread of rumors, fake
news, or diseases. However, the current state-of-the-art methods for supervised
graph classification are designed mainly for static graphs and may not be able
to capture temporal information. Hence, they are not powerful enough to
distinguish between graphs modeling different dissemination processes. To
address this, we introduce a framework to lift standard graph kernels to the
temporal domain. Specifically, we explore three different approaches and
investigate the trade-offs between loss of temporal information and efficiency.
Moreover, to handle large-scale graphs, we propose stochastic variants of our
kernels with provable approximation guarantees. We evaluate our methods on a
wide range of real-world social networks. Our methods beat static kernels by a
large margin in terms of accuracy while still being scalable to large graphs
and data sets. Hence, we confirm that taking temporal information into account
is crucial for the successful classification of dissemination processes.
