ITeM: Independent Temporal Motifs to Summarize and Compare Temporal Networks

TL;DR

This paper introduces Independent Temporal Motifs (ITeM), a novel method for characterizing and comparing temporal networks by capturing their structure and evolution through edge-disjoint motifs, outperforming existing approaches.

Contribution

The paper proposes ITeM, a new set of edge-disjoint temporal motifs, and demonstrates their effectiveness in modeling, analyzing, and comparing temporal networks across various domains.

Findings

ITeM achieves higher accuracy than other motif-based methods.

ITeM effectively captures salient properties of temporal networks.

Importance sampling efficiently estimates ITeM counts.

Abstract

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.