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

作　　者：Weijian Fan Chunhua Wang Xiao Han Chichen Lin 

机构地区：[1]School of Data Science and Intelligent Media,Communication University of China,Beijing,100024,China [2]School of Computer and Cyber Sciences,Communication University of China,Beijing,100024,China [3]Institute of Communication Studies,Communication University of China,Beijing,100024,China [4]State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing,100024,China

出　　处：《Computers, Materials & Continua》2025年第1期467-483,共17页计算机、材料和连续体（英文）

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

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 integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While 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 profiles.To 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 subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.

关 键 词：Social robot detection graph neural networks random forest HOMOPHILY heterophily 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]