Social Robot Detection Method with Improved Graph Neural Networks  

作　　者：Zhenhua Yu Liangxue Bai Ou Ye Xuya Cong 

机构地区：[1]Institute of Systems Security and Control,College of Computer Science and Technology,Xi’an University of Science and Technology,Xi’an,710054,China

出　　处：《Computers, Materials & Continua》2024年第2期1773-1795,共23页计算机、材料和连续体（英文）

基　　金：This work was supported in part by the National Natural Science Foundation of China under Grants 62273272,62303375 and 61873277;in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-243;in part by the Natural Science Foundation of Shaanxi Province under Grants 2022JQ-606 and 2020-JQ758;in part by the Research Plan of Department of Education of Shaanxi Province under Grant 21JK0752;in part by the Youth Innovation Team of Shaanxi Universities.

摘　　要：Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

关 键 词：Social robot detection social relationship subgraph graph attention network feature linear modulation behavioral gene sequences 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程]