返回
New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks
DOI:10.1007/s10479-019-03508-4.png)
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
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.5
论文数:
8.1K
被引数:
2.1W
机构
引用论文
Ascent-descent variable neighborhood decomposition search for community detection by modularity maximization通过模块化最大化进行社区检测的上升-下降可变邻域分解搜索
Patent citation analysis - A closer look at the basic input data from patent search reports
SCIENTOMETRICS
IF3.5
Regularized asymmetric nonnegative matrix factorization for clustering in directed networks有向网络聚类的正则化非对称非负矩阵分解
Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft用于自主航天器故障检测,识别和恢复的动态贝叶斯网络
Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization
SCIENTIFIC REPORTS
IF3.9
Hessian regularization based symmetric nonnegative matrix factorization for clustering gene expression and microbiome data
METHODS
IF4.3

