摘要
随着社交机器人的迭代,其倾向于与正常用户进行更多交互,对其检测变得更具挑战性。现有检测方法大多基于同配性假设,由于忽视了不同类用户间存在的联系,难以保持良好的检测性能。针对这一问题文章提出一种关注社交异配性的社交机器人检测框架,以社交网络用户间的联系为依据,通过充分挖掘用户社交信息来应对异配影响,并实现更精准的检测。文章分别在同配视角和异配视角下看待用户之间的联系,将社交网络构建为图,通过消息传递机制实现同配边和异配边聚合,以提取节点的频率特征,同时利用图中各节点特征聚合得到社交环境特征,将以上特征混合后用于检测。实验结果表明,文章所提方法在开源数据集上的检测效果优于基线方法,证明了该方法的有效性。
As social bot technology advances,these bots increasingly interact with human users,making their detection a more challenging problem.Existing detection methods primarily rely on the homophily assumption,often overlooking the connections between different classes of users,particularly the impact of heterophily.This oversight impairs their detection performance.To address this issue,this paper presented an innovative social bot detection framework that emphasizes social heterophily.It leveraged user connections within social networks and extensively explored various types of social information to mitigate the effects of heterophily and achieved more accurate detection.This paper examined user relationships from both homophily and heterophily perspectives.It constructed the social network as a graph and employed a message-passing mechanism to aggregate information from both homophilic and heterophilic edges,allowing for the extraction of frequency-based node features.Furthermore,it aggregated features from various nodes within the graph to generate social context features.These features are then blended and utilized for the detection task.The experimental results validate the method's superiority over comparative approaches on publicly available datasets,confirming its effectiveness.
作者
余尚戎
肖景博
殷琪林
卢伟
YU Shangrong;XIAO Jingbo;YIN Qilin;LU Wei(School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou 510006,China;Ministry of Education Key Laboratory of Information Technology,Sun Yat-sen University,Guangzhou 510006,China;Guangdong Province Key Laboratory of Information Security Technology,Guangzhou 510006,China)
出处
《信息网络安全》
CSCD
北大核心
2024年第2期319-327,共9页
Netinfo Security
基金
国家自然科学基金[U2001202,62072480]。
