一种融合连边符号语义信息的网络表示学习算法.PDF

37 7 Vol. 37 No. 7 第 卷第 期 计算机应用研究 录用定稿 Application Research of Computers Accepted Paper 一种融合连边符号语义信息的网络表示学习算法 * 王 凯，赵学磊，李英乐，刘正铭，李 星 ( 国家数字交换系统工程技术研究中心, 郑州 450002) 摘 要：为融合连边符号语义信息提升网络表示学习质量，针对现有算法处理复杂连边符号语义信息能力较弱问题， 提出一种融合连边符号语义信息的网络表示学习算法，将包含正负关系的连边符号语义信息引入网络表示学习过程。 首先，该算法设计基于三层感知机的关系预测模型刻画节点间不同类型的上下文链接关系；然后，引入随机游走策 略实现上下文链接采样以适应大规模网络场景训练需求。在三个数据集中实验表明，该算法能够有效建模节点间不 同类型的上下文链接关系，挖掘其中包含的复杂语义信息，相比目前最优的SIDE 方法，所提算法的性能分别提高 了0.31%、1.3%和 1.85% 。 关键词：网络表示学习；信息融合；连边符号语义信息；上下文链接 中图分类号：TP301.6 doi: 10.19734/j.issn.1001-3695.2019.01.0012 Network representation learning algorithm incorporated with edge signed semantic information Wang Kai, Zhao Xuelei, Li Yingle, Liu Zhengming, Li Xing (National Digital Switching System Engineering Technological RD Center, Zhengzhou 450002, China) Abstract: In order to enhance the network representation learning quality with the edge signed semantic information, focusing on the weakness of existing fusion methods in dealing with complex edge signed semantic information , this paper proposed a network representation learning algorithm incorporating with edge signed semantic information, and introduced the edge signed semantic information containing positive and negative relations into the network representation learning process. Firstly, this paper designed a relationship prediction model based on the three-layer perceptron to depict different types of context link relations between nodes. Then the random walk strategy was introduced to implement context link sampling