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New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks

delete2020-01-02
delete10
PRE
AI
A
Ali Tosyali
J
Jin-Ho Kim
J
Jeongsub Choi
Y
Yunyi Kang
M
Myong K. Jeong *
DOI:10.1007/s10479-019-03508-4delete
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摘要

摘要

En 中文
Outlier detection is a crucial task for network data analysis, which identifies abnormal entities that deviate from the rest of the dataset. Ranking in outlierness is often used for identifying abnormal nodes in directed citation networks containing citation relationship among nodes. A challenging issue in outlier ranking is how to leverage the rich graph data of complex citation networks. In this paper, we propose a cluster-based outlier score function to identify outliers in citation networks based on nonnegative matrix factorization (NMF). We first represent the citation data as a directed graph, and cluster the directed graph into logical groupings of nodes using NMF. Based on the clustering results, we obtain the outlier score and ranking for each node using the proposed outlier scoring function. The proposed method leverages the direct and indirect citation links between nodes to measure the graph-based outlierness. We validate the proposed outlier ranking method using small artificial dataset and the real-world U.S. patent data.
Keyword:
Anomaly detection
Directed graph
Nonnegative matrix factorization
Patent citation network
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期刊

Annals of Operations Research 封面图
Annals of Operations Research
IF:
4.5
论文数:
8.1K
被引数:
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rutgers university camden
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488
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被引数: 3
U
University of Delaware
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被引数: 2.0W
R
rutgers university system
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4.1W
论文数: 3.7W
被引数: 53
R
rutgers university new brunswick
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2.3W
论文数: 1.9W
被引数: 32
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