Temporal networks encode interactions between entities as well as the time at which the interactions took place, allowing the user to identify systematic processes within the network. The authors can identify sub-processes or temporal motifs that recur frequently across a large network. In this paper, they present a strategy that allows the user to identify which of a given set of temporal processes are over-represented. This highlights peculiarities of behavior in the network. Their strategy involves constructing a set of interesting temporal processes, counting their embeddings in the network through sub-graph matching, and then comparing this against counts in a temporally random version of the network.