TY - GEN
T1 - Ranking causal anomalies by modeling local propagations on networked systems
AU - Ni, Jingchao
AU - Cheng, Wei
AU - Zhang, Kai
AU - Song, Dongjin
AU - Yan, Tan
AU - Chen, Haifeng
AU - Zhang, Xiang
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially supported by the National Science Foundation grants IIS-1664629 and CAREER.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network, a node represents a system component, and an edge indicates a stable interaction between two components. Recent approaches have shown that by modeling fault propagation in the invariant network, causal anomalies can be effectively discovered. Despite their success, the existing methods have a major limitation: they typically assume there is only a single and global fault propagation in the entire network. However, in real-world large-scale complex systems, it's more common for multiple fault propagations to grow simultaneously and locally within different node clusters and jointly define the system failure status. Inspired by this key observation, we propose a two-phase framework to identify and rank causal anomalies. In the first phase, a probabilistic clustering is performed to uncover impaired node clusters in the invariant network. Then, in the second phase, a low-rank network diffusion model is designed to backtrack causal anomalies in different impaired clusters. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.
AB - Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network, a node represents a system component, and an edge indicates a stable interaction between two components. Recent approaches have shown that by modeling fault propagation in the invariant network, causal anomalies can be effectively discovered. Despite their success, the existing methods have a major limitation: they typically assume there is only a single and global fault propagation in the entire network. However, in real-world large-scale complex systems, it's more common for multiple fault propagations to grow simultaneously and locally within different node clusters and jointly define the system failure status. Inspired by this key observation, we propose a two-phase framework to identify and rank causal anomalies. In the first phase, a probabilistic clustering is performed to uncover impaired node clusters in the invariant network. Then, in the second phase, a low-rank network diffusion model is designed to backtrack causal anomalies in different impaired clusters. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.
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U2 - 10.1109/ICDM.2017.129
DO - 10.1109/ICDM.2017.129
M3 - Conference contribution
AN - SCOPUS:85043976130
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1003
EP - 1008
BT - Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
A2 - Karypis, George
A2 - Alu, Srinivas
A2 - Raghavan, Vijay
A2 - Wu, Xindong
A2 - Miele, Lucio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Data Mining, ICDM 2017
Y2 - 18 November 2017 through 21 November 2017
ER -