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EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection

文献类型：期刊论文

作者Feng, Wenjie2,3; Liu, Shenghua2,3; Faloutsos, Christos4; Hooi, Bryan1; Shen, Huawei2,3; Cheng, Xueqi2,3
刊名FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
出版日期2021-02-01
卷号115页码:236-250
关键词Pattern recognition Large graph mining Micro-clusters Anomaly detection Histogram
ISSN号0167-739X
DOI10.1016/j.future.2020.08.033
英文摘要Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics? How can we detect them and separate anomalous nodes in a way similar to human visual perception? More generally, how can we identify micro-clusters in a histogram and spot some interesting patterns? In this paper, we propose a vision-guided algorithm, EagleMine, to recognize and summarize node groups in a histogram constructed from some correlated features. EagleMine hierarchically discovers node groups, which form internally connected dense areas in the histogram, by utilizing a water -level tree with multiple resolutions according to the rule of the visual recognition. EagleMine uses the statistical hypothesis test to determine the optimal groups while exploring the tree and simultaneously performs vocabulary-based summarization. Moreover, EagleMine can identify anomalous micro-clusters, consisting of nodes that exhibit very similar and suspicious behavior, deviate away from the majority. Experiments on the real-world datasets show that our method can recognize intuitive node groups as human vision does; it achieves the best summarization performance compared to baselines. In terms of anomaly detection, EagleMine also outperforms the state-of-the-art graph-based methods with significantly improving accuracy in a micro-blog dataset. Moreover, EagleMine can be used for other applications, e.g., to detect the synchronized patterns in the temporal retweet event. (c) 2020 Elsevier B.V. All rights reserved.
资助项目Strategic Priority Research Program of CAS, China[XDA19020400] ; NSF of China[61772498] ; NSF of China[61425016] ; NSF of China[91746301] ; NSF of China[61872206] ; Beijing NSF, China[4172059]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000591438600010
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/16493]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Feng, Wenjie; Liu, Shenghua
作者单位1.Natl Univ Singapore, Dept Comp Sci, Singapore, Singapore
2.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China
4.Carnegie Mellon Univ, Comp Sci Dept, Pittsburgh, PA 15213 USA
推荐引用方式
GB/T 7714		 Feng, Wenjie,Liu, Shenghua,Faloutsos, Christos,et al. EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2021,115:236-250.
APA Feng, Wenjie,Liu, Shenghua,Faloutsos, Christos,Hooi, Bryan,Shen, Huawei,&Cheng, Xueqi.(2021).EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,115,236-250.
MLA Feng, Wenjie,et al."EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 115(2021):236-250.

入库方式： OAI收割

来源：计算技术研究所

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