ITeM: Independent temporal motifs to summarize and compare temporal networks

Purohit, Sumit; Chin, George; Holder, Lawrence B.

doi:10.3233/ida-205698

ITeM: Independent temporal motifs to summarize and compare temporal networks

Journal Article · 10 July 2022 · Intelligent Data Analysis

DOI:https://doi.org/10.3233/ida-205698· OSTI ID:1887214

^[1]; Chin, George ^[1]; Holder, Lawrence B. ^[2]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Washington State University, Pullman, WA (United States)

We report 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 every relationship occurs at a discrete time. The temporal evolution of such networks 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. ITeMs can be used to model the structure and the evolution of the graph. In contrast to existing work, ITeMs are edge-disjoint directed motifs that measure the temporal evolution of ordered edges within the motif. For a given temporal graph, we produce a feature vector of ITeM frequencies and the time it takes to form the ITeM instances. We apply this distribution to measure the similarity of temporal graphs. We show that ITeM has higher accuracy than other motif frequency-based approaches. We define various ITeM-based metrics that reveal salient properties of a temporal network. We also present importance sampling as a method to efficiently estimate the ITeM counts. We present a distributed implementation of the ITeM discovery algorithm using Apache Spark and GraphFrame. We evaluate our approach on both synthetic and real temporal networks.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; Defense Advanced Research Projects Agency (DARPA)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1887214

Report Number(s):: PNNL-SA-158727

Journal Information:: Intelligent Data Analysis, Journal Name: Intelligent Data Analysis Journal Issue: 4 Vol. 26; ISSN 1088-467X

Publisher:: IOS PressCopyright Statement

Country of Publication:: United States

Language:: English

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