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Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection

Authors
  • Saebi, Mandana1
  • Xu, Jian1
  • Kaplan, Lance M.2
  • Ribeiro, Bruno3
  • Chawla, Nitesh V.1
  • 1 University of Notre Dame, Notre Dame, USA , Notre Dame (United States)
  • 2 U.S. Army Research Lab, Adelphi, USA , Adelphi (United States)
  • 3 Purdue University, West Lafayette, USA , West Lafayette (United States)
Type
Published Article
Journal
EPJ Data Science
Publisher
Springer Berlin Heidelberg
Publication Date
Jun 09, 2020
Volume
9
Issue
1
Identifiers
DOI: 10.1140/epjds/s13688-020-00233-y
Source
Springer Nature
Keywords
License
Green

Abstract

Complex systems, represented as dynamic networks, comprise of components that influence each other via direct and/or indirect interactions. Recent research has shown the importance of using Higher-Order Networks (HONs) for modeling and analyzing such complex systems, as the typical Markovian assumption in developing the First Order Network (FON) can be limiting. This higher-order network representation not only creates a more accurate representation of the underlying complex system, but also leads to more accurate network analysis. In this paper, we first present a scalable and accurate model, BuildHON+, for higher-order network representation of data derived from a complex system with various orders of dependencies. Then, we show that this higher-order network representation modeled by BuildHON+ is significantly more accurate in identifying anomalies than FON, demonstrating a need for the higher-order network representation and modeling of complex systems for deriving meaningful conclusions.

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