  Numerous social, medical, engineering and biological challenges can be framed
as graph-based learning tasks. Here, we propose a new feature based approach to
network classification. We show how dynamics on a network can be useful to
reveal patterns about the organization of the components of the underlying
graph where the process takes place. We define generalized assortativities on
networks and use them as generalized features across multiple time scales.
These features turn out to be suitable signatures for discriminating between
different classes of networks. Our method is evaluated empirically on
established network benchmarks. We also introduce a new dataset of human brain
networks (connectomes) and use it to evaluate our method. Results reveal that
our dynamics based features are competitive and often outperform state of the
art accuracies.

Machine Learning