TY - GEN
T1 - Identify Spammers in Rating Systems Using Multi-layer Graph Convolutional Network
AU - Huang, Jia Tao
AU - Sun, Hong Liang
AU - Cao, Jie
AU - Yi, Lan
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/12/14
Y1 - 2021/12/14
N2 - With the rise of deep learning technology, researches on the application of graph convolutional networks to fraud detection emerge endlessly. However, the graph convolutional network used often has only two layers, which makes us unable to obtain higher-order node information. So we must use a multi-layer graph neural network to get more node information for training in order to get more accurate detection results. Using a multi-layer graph neural network also causes gradient disappearance; that is, the model parameters cannot be updated, and the model is invalid. This work explores the feasibility of using multi-layer GCN to detect fraudsters from the internal structure of GCN, that is, the number of hidden layer neurons and the activation function. Finally, it is tested on real data sets, and the detection accuracy of fraudsters using multi-layer GCN is increased by about 14.6% at most.
AB - With the rise of deep learning technology, researches on the application of graph convolutional networks to fraud detection emerge endlessly. However, the graph convolutional network used often has only two layers, which makes us unable to obtain higher-order node information. So we must use a multi-layer graph neural network to get more node information for training in order to get more accurate detection results. Using a multi-layer graph neural network also causes gradient disappearance; that is, the model parameters cannot be updated, and the model is invalid. This work explores the feasibility of using multi-layer GCN to detect fraudsters from the internal structure of GCN, that is, the number of hidden layer neurons and the activation function. Finally, it is tested on real data sets, and the detection accuracy of fraudsters using multi-layer GCN is increased by about 14.6% at most.
KW - Deep Learning
KW - Fraud Detection
KW - Graph Convolutional Network
KW - Over-smoothing.
UR - http://www.scopus.com/inward/record.url?scp=85128534190&partnerID=8YFLogxK
U2 - 10.1145/3498851.3498976
DO - 10.1145/3498851.3498976
M3 - Conference contribution
AN - SCOPUS:85128534190
T3 - ACM International Conference Proceeding Series
SP - 340
EP - 346
BT - Proceedings of 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops and Special Sessions, WI-IAT 2021
A2 - Gao, Xiaoying
A2 - Huang, Guangyan
A2 - Cao, Jie
A2 - Cao, Jian
A2 - Deng, Ke
PB - Association for Computing Machinery
T2 - 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
Y2 - 14 December 2021 through 17 December 2021
ER -
