Identify Spammers in Rating Systems Using Multi-layer Graph Convolutional Network

Jia Tao Huang, Hong Liang Sun, Jie Cao, Lan Yi

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops and Special Sessions, WI-IAT 2021
EditorsXiaoying Gao, Guangyan Huang, Jie Cao, Jian Cao, Ke Deng
PublisherAssociation for Computing Machinery
Pages340-346
Number of pages7
ISBN (Electronic)9781450391870
DOIs
Publication statusPublished - 14 Dec 2021
Externally publishedYes
Event2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021 - Virtual, Online, Australia
Duration: 14 Dec 202117 Dec 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
Country/TerritoryAustralia
CityVirtual, Online
Period14/12/2117/12/21

Keywords

  • Deep Learning
  • Fraud Detection
  • Graph Convolutional Network
  • Over-smoothing.

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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