Semi-GSGCN: Social Robot Detection Research with Graph Neural Network |
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Authors: | Xiujuan Wang Qianqian Zheng Kangfeng Zheng Yi Sui Jiayue Zhang |
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Affiliation: | 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. |
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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. |
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Keywords: | Social networks social robot detection network representation learning graph convolution network |
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