北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (5): 77-83.doi: 10.13190/j.jbupt.2020-074

• 论文 • 上一篇    下一篇

一种用于图卷积网络的社交关系方向门控算法

李蕾, 谢旸, 蒋亚飞, 刘咏彬   

  1. 北京邮电大学 人工智能学院, 北京 100876
  • 收稿日期:2020-06-25 发布日期:2021-03-11
  • 作者简介:李蕾(1974-),女,副教授,硕士生导师,E-mail:leili@bupt.edu.cn.
  • 基金资助:
    国家自然科学基金项目(91546121,71231002);北京市科学技术委员会项目(Z181100001018035)

A Social Relationship Direction Gating Algorithm for Graph Convolutional Networks

LI Lei, XIE Yang, JIANG Ya-fei, LIU Yong-bin   

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2020-06-25 Published:2021-03-11

摘要: 针对社交网络用户态度分析任务中用户之间原有社交关系方向可能阻碍态度信息流动以及标签扩散的问题,提出了一种应用于半监督图卷积网络的社交关系方向门控算法.该算法首先在原有与逆向社交关系方向上分别进行图卷积运算,得到2种用户节点态度特征向量,然后利用门控机制对2种特征向量进行动态融合.扩展了态度信息传播路径的同时,还能够捕捉用户影响力差异,以自动选择态度信息的流动方向.在2个真实热点话题数据集上的实验结果表明,现有图卷积网络在加入该算法之后,其用户态度分析的准确率能够得到有效提升.

关键词: 图卷积网络, 门控算法, 用户态度分析, 社交网络

Abstract: Facing the problem in social user attitude analysis that the original social relationship direction between users in social networks hinders the flow of attitude information and label propagation, a social relationship direction gating algorithm for graph convolutional networks is proposed. The algorithm first performs graph convolution on the origin and reverse social relationship directions to obtain two types of user node attitude feature vectors, and then leverages the gating mechanism to integrate the feature vectors dynamically. While expanding the propagation of attitude information, the algorithm can also capture the differences of user influence to automatically select the flow of attitude information. Experiments on two real hot topic datasets show that the accuracy of the existing graph convolutional networks can be effectively improved after adding the proposed algorithm.

Key words: graph convolutional network, gating algorithm, user attitude analysis, social network

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