@Article{cmc.2020.011165,
AUTHOR = {Xiujuan Wang, Qianqian Zheng, Kangfeng Zheng, Yi Sui, Jiayue Zhang},
TITLE = {Semi-GSGCN: Social Robot Detection Research with Graph  Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {617--638},
URL = {http://www.techscience.com/cmc/v65n1/39586},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011165}
}