  Networks are widely used to model real-world systems and uncover their
topological features. Network properties such as the degree distribution and
shortest path length have been computed in numerous real-world networks, and
most of them have been shown to be both scale-free and small-world networks.
Graphlets and network motifs are subgraph patterns that capture richer
structural information than aforementioned global network properties, and these
local features are often used for network comparison. However, past work on
graphlets and network motifs is almost exclusively applicable only for static
networks. Many systems are better represented as temporal networks which depict
not only how a system was at a given stage but also how they evolved.
Time-dependent information is crucial in temporal networks and, by disregarding
that data, static methods can not achieve the best possible results. This paper
introduces an extension of graphlets for temporal networks. Our proposed method
enumerates all 4-node graphlet-orbits in each network-snapshot, building the
corresponding orbit-transition matrix in the process. Our hypothesis is that
networks representing similar systems have characteristic orbit transitions
which better identify them than simple static patterns, and this is assessed on
a set of real temporal networks split into categories. In order to perform
temporal network comparison we put forward an orbit-transition-agreement metric
(OTA). OTA correctly groups a set of temporal networks that both static network
motifs and graphlets fail to do so adequately. Furthermore, our method produces
interpretable results which we use to uncover characteristic orbit transitions,
and that can be regarded as a network-fingerprint.
