  Temporal networks are commonly used to represent systems where connections
between elements are active only for restricted periods of time, such as
networks of telecommunication, neural signal processing, biochemical reactions
and human social interactions. We introduce the framework of temporal motifs to
study the mesoscale topological-temporal structure of temporal networks in
which the events of nodes do not overlap in time. Temporal motifs are classes
of similar event sequences, where the similarity refers not only to topology
but also to the temporal order of the events. We provide a mapping from event
sequences to colored directed graphs that enables an efficient algorithm for
identifying temporal motifs. We discuss some aspects of temporal motifs,
including causality and null models, and present basic statistics of temporal
motifs in a large mobile call network.
