ITeM: Independent temporal motifs to summarize and compare temporal networks

Networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, transportation can be modeled set entities their relationships. Temporal networks specialization general where every relationship occurs at discrete time. The temporal evolution is important to understand the structure We present Independent Motif (ITeM) characterize graphs from different domains. ITeMs used model graph. In contrast existing work, edge-disjoint directed motifs that measure ordered edges within motif. For given graph, we produce feature vector ITeM frequencies time it takes form instances. apply this distribution similarity graphs. show has higher accuracy than other motif frequency-based approaches. define ITeM-based metrics reveal salient properties network. also importance sampling method efficiently estimate counts. distributed implementation discovery algorithm using Apache Spark GraphFrame. evaluate our approach on both synthetic real networks.