An anomaly aware network embedding framework for unsupervised anomalous link detection

doi:10.1007/s10618-023-00960-6

	An anomaly aware network embedding framework for unsupervised anomalous link detection
	Duan, Dongsheng 1; Zhang, Cheng 2; Tong, Lingling 1; Lu, Jie 2; Lv, Cunchi 2; Hou, Wei 1; Li, Yangxi 1; Zhao, Xiaofang 2
	2023-08-19
发表期刊	DATA MINING AND KNOWLEDGE DISCOVERY
ISSN	1384-5810
页码	34
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.
关键词	Anomalous link detection Network embedding Graph auto-encoder Graph convolution network
DOI	10.1007/s10618-023-00960-6
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:001050238700001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21366
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.