ITeM: Independent Temporal Motifs to Summarize and Compare Temporal Networks
This work addresses the need for better summarization and comparison tools for temporal networks across domains like communication and social media, representing an incremental improvement over existing motif-based methods.
The authors tackled the problem of characterizing and comparing temporal networks by introducing Independent Temporal Motifs (ITeMs), which are edge-disjoint temporal motifs used to model graph structure and evolution, resulting in higher accuracy than other motif frequency-based approaches for measuring similarity.
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.