  Networks are a fundamental and flexible way of representing various complex
systems. Many domains such as communication, citation, procurement, biology,
social media, and transportation can be modeled as a set of entities and their
relationships. Temporal networks are a specialization of general networks where
the temporal evolution of the system is as important to understand as the
structure of the entities and relationships. We present the Independent
Temporal Motif (ITeM) to characterize temporal graphs from different domains.
The ITeMs are edge-disjoint temporal motifs that can be used to model the
structure and the evolution of the graph. For a given temporal graph, we
produce a feature vector of ITeM frequencies and apply this distribution to the
task of measuring the similarity of temporal graphs. We show that ITeM has
higher accuracy than other motif frequency-based approaches. We define various
metrics based on ITeM that reveal salient properties of a temporal network. We
also present importance sampling as a method for efficiently estimating the
ITeM counts. We evaluate our approach on both synthetic and real temporal
networks.

Social and Information Networks;Artificial Intelligence