北京大学学报（自然科学版）2025，Vol.61Issue(4)：639-649,11.DOI:10.13209/j.0479-8023.2024.120
基于社交时序知识图谱的推特机器人检测方法
Twitter Bot Detection Method Based on Social Temporal Knowledge Graph
摘要
Abstract
Existing Twitter bot detection methods often overlook the structural and temporal information of users'dynamic social history,as well as the noise accumulation resulting from feature fusion.In order to address these limitations,this paper constructs STKG(social temporal knowledge graph)and proposes a Twitter bot detection method STKGBot(STKG for Twitter bot detection).In the STKG,STKGBot uses RE-GAT(heterogeneity-enhanced graph attention network)to learn the static social relationship feature,TE-GCN(temporal-enhanced graph convo-lutional network)to learn the dynamic social history feature,and a bilinear model for the feature fusion.In addition,STKGBot employs contrastive learning to alleviate the noise aggravation in the process of feature fusion.Experi-mental results on two public datasets demonstrate that STKGBot outperforms state-of-the-art models.关键词
推特机器人检测/时序知识图谱/图神经网络/对比学习Key words
Twitter bot detection/temporal knowledge graph/GNN/contrastive learning引用本文复制引用
蒋致书,陈炜,张伟杰,张诗琪,陈季若,万怀宇..基于社交时序知识图谱的推特机器人检测方法[J].北京大学学报（自然科学版）,2025,61(4):639-649,11.基金项目
国家重点研发计划(2021QY1502)资助 （2021QY1502）
