most citedUncovering Insurance Fraud Conspiracy with Network Learning

49 citations · 79 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SI20209 cited

Heterogeneous Graph Neural Network for Recommendation

Jinghan Shi, Houye Ji, Chuan Shi +3

The prosperous development of e-commerce has spawned diverse recommendation systems. As a matter of fact, there exist rich and complex interactions among various types of nodes in…

cs.LG202014 cited

Secure Social Recommendation based on Secret Sharing

Chaochao Chen, Liang Li, Bingzhe Wu +3

Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many com…

cs.CV20201 cited

Data-Free Adversarial Perturbations for Practical Black-Box Attack

ZhaoXin Huan, Yulong Wang, Xiaolu Zhang +3

Neural networks are vulnerable to adversarial examples, which are malicious inputs crafted to fool pre-trained models. Adversarial examples often exhibit black-box attacking transf…

cs.SI2020

Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing

Ziqi Liu, Dong Wang, Qianyu Yu +8

Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited…

cs.CR202049 cited

Uncovering Insurance Fraud Conspiracy with Network Learning

Chen Liang, Ziqi Liu, Bin Liu +4

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over…

cs.LG20206 cited

Heterogeneous Graph Neural Networks for Malicious Account Detection

Ziqi Liu, Chaochao Chen, Xinxing Yang +3

We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform. Our…