Exploring Correlation Network for Cheating Detection

doi:10.1145/3364221

	Exploring Correlation Network for Cheating Detection
	Luo, Ping 1,2; Shu, Kai 3; Wu, Junjie 4,5; Wan, Li 6; Tan, Yong 7
	2020-02-01
发表期刊	ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
ISSN	2157-6904
卷号	11 期号:1 页码:23
摘要	The correlation network, typically formed by computing pairwise correlations between variables, has recently become a competitive paradigm to discover insights in various application domains, such as climate prediction, financial marketing, and bioinformatics. In this study, we adopt this paradigm to detect cheating behavior hidden in business distribution channels, where falsified big deals are often made by collusive partners to obtain lower product prices-a behavior deemed to be extremely harmful to the sale ecosystem. To this end, we assume that abnormal deals are likely to occur between two partners if their purchase-volume sequences have a strong negative correlation. This seemingly intuitive rule, however, imposes several research challenges. First, existing correlation measures are usually symmetric and thus cannot distinguish the different roles of partners in cheating. Second, the tick-to-tick correspondence between two sequences might be violated due to the possible delay of purchase behavior, which should also be captured by correlation measures. Finally, the fact that any pair of sequences could be correlated may result in a number of false-positive cheating pairs, which need to be corrected in a systematic manner. To address these issues, we propose a correlation network analysis framework for cheating detection. In the framework, we adopt an asymmetric correlation measure to distinguish the two roles, namely, cheating seller and cheating buyer, in a cheating alliance. Dynamic TimeWarping is employed to address the time offset between two sequences in computing the correlation. We further propose two graph-cut methods to convert the correlation network into a bipartite graph to rank cheating partners, which simultaneously helps to remove false-positive correlation pairs. Based on a 4-year real-world channel dataset from a worldwide IT company, we demonstrate the effectiveness of the proposed method in comparison to competitive baseline methods.
关键词	Correlation network analysis cheating detection distribution channel time series graph cut
DOI	10.1145/3364221
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002104] ; National Natural Science Foundation of China (NSFC)[U1811461] ; Innovation Program of Institute of Computing Technology, CAS ; National Key R&D Program of China[2019YFB2101804] ; NSFC[71729001] ; NSFC[71725002] ; NSFC[71531001] ; NSFC[U1636210] ; NSFC[71471009] ; NSFC[71490723]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000535726400012
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15309
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wu, Junjie
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 Kexueyuan South Rd Zhongguancun, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Arizona State Univ, Dept Comp Sci & Engn, 561BB Brickyard Suit,699 S Mill Ave, Tempe, AZ 85281 USA 4.Beihang Univ, Sch Econ & Management, 37 Xue Yuan Rd, Beijing 100191, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, 37 Xue Yuan Rd, Beijing 100191, Peoples R China 6.Chongqing Univ, Dept Comp Sci & Technol, 174 ShaZheng Rd, Chongqing 400034, Peoples R China 7.Univ Washington, Dept Informat Syst & Operat Management, 1410 NE Campus Pkwy, Seattle, WA 98195 USA
推荐引用方式 GB/T 7714	Luo, Ping,Shu, Kai,Wu, Junjie,et al. Exploring Correlation Network for Cheating Detection[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2020,11(1):23.
APA	Luo, Ping,Shu, Kai,Wu, Junjie,Wan, Li,&Tan, Yong.(2020).Exploring Correlation Network for Cheating Detection.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,11(1),23.
MLA	Luo, Ping,et al."Exploring Correlation Network for Cheating Detection".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 11.1(2020):23.