  Understanding how information propagates in real-life complex networks yields
a better understanding of dynamic processes such as misinformation or epidemic
spreading. The recently introduced branch of machine learning methods for
learning node representations offers many novel applications, one of them being
the task of spreading prediction addressed in this paper. We explore the
utility of the state-of-the-art node representation learners when used to
assess the effects of spreading from a given node, estimated via extensive
simulations. Further, as many real-life networks are topologically similar, we
systematically investigate whether the learned models generalize to previously
unseen networks, showing that in some cases very good model transfer can be
obtained. This work is one of the first to explore transferability of the
learned representations for the task of node regression; we show there exist
pairs of networks with similar structure between which the trained models can
be transferred (zero-shot), and demonstrate their competitive performance. To
our knowledge, this is one of the first attempts to evaluate the utility of
zero-shot transfer for the task of node regression.
