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An anomaly aware network embedding framework for unsupervised anomalous link detection

文献类型：期刊论文

作者Duan, Dongsheng1; Zhang, Cheng2; Tong, Lingling1; Lu, Jie2; Lv, Cunchi2; Hou, Wei1; Li, Yangxi1; Zhao, Xiaofang2
刊名DATA MINING AND KNOWLEDGE DISCOVERY
出版日期2023-08-19
页码34
关键词Anomalous link detection Network embedding Graph auto-encoder Graph convolution network
ISSN号1384-5810
DOI10.1007/s10618-023-00960-6
英文摘要Most existing network embedding based anomalous link detection methods regard network embedding and anomalous link detection as two independent tasks. However, removing anomalous links from the original network can reduce the data noise, thus hopefully improving the performance of network embedding models and anomalous link detection. In this paper, we propose an Anomaly Aware Network Embedding (AANE) framework by simultaneously learning node embedding and detecting anomalous links in a unified way. To instantiate the AANE framework, we propose a heuristic anomalous link selection based model AANE-H and an embedding disentangling based model AANE-D on Graph Auto-Encoder (GAE). In AANE-H, we adopt an anomalous link selector to iteratively select significant anomalous links based on a heuristic rule during model training, while in AANE-D the normal and anomalous links are generated by disentangled normal and anomalous embedding respectively. For the evaluation purpose, we propose a heuristic anomalous link generation algorithm to inject synthetic anomalous links into six real-world network datasets used in our experiments. Experimental results show that AANE outperforms both the state-of-the-art network embedding models and anomalous node detection models in terms of anomalous link detection performance. As a general network embedding model, AANE can also improve other downstream tasks like node classification.
资助项目National Natural Science Foundation of China[62272125] ; National Natural Science Foundation of China[62192785] ; National Natural Science Foundation of China[U1836111] ; National Natural Science Foundation of China[U1936110]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001050238700001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/21365]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Duan, Dongsheng; Zhang, Cheng
作者单位1.Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, A3 Yuming Rd, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100086, Peoples R China
推荐引用方式
GB/T 7714		 Duan, Dongsheng,Zhang, Cheng,Tong, Lingling,et al. An anomaly aware network embedding framework for unsupervised anomalous link detection[J]. DATA MINING AND KNOWLEDGE DISCOVERY,2023:34.
APA Duan, Dongsheng.,Zhang, Cheng.,Tong, Lingling.,Lu, Jie.,Lv, Cunchi.,...&Zhao, Xiaofang.(2023).An anomaly aware network embedding framework for unsupervised anomalous link detection.DATA MINING AND KNOWLEDGE DISCOVERY,34.
MLA Duan, Dongsheng,et al."An anomaly aware network embedding framework for unsupervised anomalous link detection".DATA MINING AND KNOWLEDGE DISCOVERY (2023):34.

入库方式： OAI收割

来源：计算技术研究所

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