Unmasking Social Robots’Camouflage:A GNN-Random Forest Framework for Enhanced Detection

作     者：Weijian Fan Chunhua Wang Xiao Han Chichen Lin 

作者机构：School of Data Science and Intelligent MediaCommunication University of ChinaBeijing100024China School of Computer and Cyber SciencesCommunication University of ChinaBeijing100024China Institute of Communication StudiesCommunication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and CommunicationCommunication University of ChinaBeijing100024China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2025年第82卷第1期

页      面：467-483页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基　　金：Funds for the Central Universities(grant number CUC24SG018) 

主　　题：Social robot detection graph neural networks random forest homophily heterophily 

摘      要：The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content *** robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading *** graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human *** unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing ***,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored *** culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection *** experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.