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

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 assumpt...

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Bibliographic Details
Published in:EPJ data science 2020-06, Vol.9 (1), p.15-22, Article 15
Main Authors: Saebi, Mandana, Xu, Jian, Kaplan, Lance M., Ribeiro, Bruno, Chawla, Nitesh V.
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Anomaly detection
Complex systems
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer Science
Data science
Data-driven Science
Dynamic network
Higher-order network
Modeling and Theory Building
Modelling
Network analysis
Regular Article
Representations
Sequential data
User behavior
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Summary: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.
ISSN:2193-1127
2193-1127
DOI:10.1140/epjds/s13688-020-00233-y