网络中高阶依赖关系的有效建模：从算法到异常检测应用
Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection
摘要：
复杂系统，表现为动态网络，包括通过直接和/或间接相互作用相互影响的组件。最近的研究表明，使用高阶网络（HONS）建模和分析这样的复杂系统的重要性，因为在开发一阶网络（FON）的典型马尔可夫假设可能是有限的。这种高阶网络表示不仅可以更准确地表示底层复杂系统，还可以进行更准确的网络分析。在本文中，我们首先提出了一个可扩展的和准确的模型，BuildHON+，高阶网络表示的数据来自一个复杂的系统与各种顺序的依赖关系。然后，我们表明，这种高阶网络表示建模的BuildHON+是显着更准确地识别异常比FON，证明了需要高阶网络表示和复杂系统的建模，以得出有意义的结论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
