  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.
