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The graph-based model can help to detect suspicious fraud online.
Collective Opinion Spam Detection: Bridging Review Networks and Metadata. In KDD
S. Rayana and L. Akoglu. 2015 · 2015
Earlier work this paper cites.
Suspicious behavior detection: Current trends and future directions
M. Jiang, P. Cui, and C. Faloutsos. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs. In NeurIPS
W. Hamilton, Z. Ying, and J. Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks. In ICLR
T.N. Kipf and M. Welling. 2017 · 2017
Earlier work this paper cites.
Graph attention networks. In ICLR
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2017 · 2017
Earlier work this paper cites.
Combating crowdsourced review manipulators: A neighborhood-based approach. In WSDM
P. Kaghazgaran, J. Caverlee, and A. Squicciarini. 2018 · 2018
Cited alongside, same era.
Heterogeneous Graph Neural Networks for Malicious Account Detection. In CIKM
Z. Liu, C. Chen, X. Yang, J. Zhou, X. Li, and L. Song. 2018 · 2018
Cited alongside, same era.
Adversarial Attack and Defense on Graph Data: A Survey
L. Sun, Y. Dou, C. Yang, J. Wang, P. S. Yu, and B. Li. 2018 · 2018
Cited alongside, same era.
Uncovering download fraud activities in mobile app markets. In ASONAM
Y. Dou, W. Li, Z. Liu, Z. Dong, J. Luo, and P. S. Yu. 2019 · 2019
Cited alongside, same era.
Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures. In CIKM
P. Kaghazgaran, M. Alfifi, and J. Caverlee. 2019 · 2019
Cited alongside, same era.
A Semi-supervised Graph Attentive Network for Fraud Detection. In ICDM
D. Wang, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, S. Yang, and Y. Qi. 2019a
Cited in the paper.
FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System. In WWW Workshops
J. Wang, R. Wen, C. Wu, Y. Huang, and J. Xion. 2019b
Cited in the paper.
Spam Review Detection with Graph Convolutional Networks. In CIKM
A. Li, Z. Qin, R. Liu, Y. Yang, and D. Li. 2019 · 2019
Later among the works it cites.
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks. In IJCAI
H. Peng, J. Li, Q. Gong, Y. Song, Y. Ning, K. Lai, and P. S. Yu. 2019 · 2019
Later among the works it cites.
Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework. In CIKM
Y. Zhang, Y. Fan, Y. Ye, L. Zhao, and C. Shi. 2019 · 2019
Later among the works it cites.
Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR
Y. Hou, J. Zhang, J. Cheng, K. Ma, R. T. B. Ma, H. Chen, and M. Yang. 2020 · 2020
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Q. Zhong, Y. Liu, X. Ao, B. Hu, J. Feng, J. Tang, and Q. He. 2020 · 2020
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