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Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

Xiujuan Wang1, Qianqian Zheng1, *, Kangfeng Zheng2, Yi Sui1, Jiayue Zhang1

1 Information Technology Institute, Beijing University of Technology, Beijing, 100124, China.
2 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

* Corresponding Author: Qianqian Zheng. Email: .

Computers, Materials & Continua 2020, 65(1), 617-638.


Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods, in this paper a robot detection scheme based on weighted network topology is proposed, which introduces an improved network representation learning algorithm to extract the local structure features of the network, and combined with the graph convolution network (GCN) algorithm based on the graph filter, to obtain the global structure features of the network. An end-to-end semi-supervised combination model (Semi-GSGCN) is established to detect malicious social robots. Experiments on a social network dataset (cresci-rtbust-2019) show that the proposed method has high versatility and effectiveness in detecting social robots. In addition, this method has a stronger insight into robots in social networks than other methods.


Cite This Article

X. Wang, Q. Zheng, K. Zheng, Y. Sui and J. Zhang, "Semi-gsgcn: social robot detection research with graph neural network," Computers, Materials & Continua, vol. 65, no.1, pp. 617–638, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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