Detecting Anomalies in Sequential Data with Higher-order Networks

27 Dec 2017Jian XuMandana SaebiBruno RibeiroLance M. KaplanNitesh V. Chawla

A major branch of anomaly detection methods relies on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure. However, existing approaches use first-order networks (FONs) to represent the underlying raw data, which may lose important higher-order sequence patterns, making higher-order anomalies undetectable in subsequent analysis... (read more)

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