摘要
针对异质网络表示学习中邻接节点表示向量的融合问题,提出基于多臂老虎机的异质网络表示学习方法。该方法采用基于多臂老虎机思想,实现异质网络中元路径关系的权重的自适应计算,在节点分类任务上取得的Micro-F1值(89.56%和54.79%)和Macro-F1值(89.09%和53.14%)均优于基准测试。面对节点信息的多样性,基于多臂老虎机的网络表示学习方法能够将网络结构和节点信息更加有效地融入图的表示学习中。
To deal with the fusion of adjacent node representation vectors in heterogeneous network representation learn-ing,an adaptive calculation of relationship weights of meta-path relations in heterogeneous networks is achieved by adopting the idea of multi-armed bandit.By introducing the above measures,this method achieved Micro-F1 values of 89.56%and 54.79%,as well as Macro-F1 values of 89.09%and 53.14%in node classification tasks.The research results show that in the face of the diversity of node information,the network representation learning method based on multi-armed bandit can more effectively integrate network structure and node information into graph representation learning.
作者
闫旸
陈泽秋
邓钧霖
YAN Yang;CHEN Zeqiu;DENG Junlin(School of Information Technology and Engineering,Tianjin University of Technology and Education,Tianjin 300222,China)
出处
《天津职业技术师范大学学报》
2024年第1期61-65,共5页
Journal of Tianjin University of Technology and Education
基金
教育部人文社会科学研究规划基金青年基金项目(22YJC870018)
天津市教委科研计划项目(2020KJ112)
应用数学福建省高校重点实验室开放课题(SX201904)
天津职业技术师范大学人才启动项目(KYQD1817).
关键词
多臂老虎机模型
异质网络
网络表示学习
自适应权重
muti-arm bandit
heterogeneous network
network representation
self-adaptive weight