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

TL;DR

This paper introduces BuildHON+, a scalable method for creating higher-order network models that better capture complex system dependencies, leading to improved anomaly detection accuracy.

Contribution

The paper presents BuildHON+, a novel scalable algorithm for higher-order network modeling that enhances anomaly detection in complex systems.

Findings

BuildHON+ outperforms First Order Networks in anomaly detection accuracy.

Higher-order network modeling captures complex dependencies more effectively.

The approach is scalable to large, real-world datasets.

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.