Open Access
ARTICLE
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. https://doi.org/10.32604/cmc.2020.011165
Received 23 April 2020; Accepted 31 May 2020; Issue published 23 July 2020
Abstract
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.
Keywords
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. https://doi.org/10.32604/cmc.2020.011165