一种基于层级信息优化的有向网络表示学习方法

网络表示方法旨在将每个节点映射到低维向量空间,并保留节点在网络中的结构关系。有向网络的环中节点相互可达,破坏了非对称传递性,影响了模型对网络整体结构信息的学习。为削弱有向网络的环在表示学习中的影响,增强模型对全局结构信息的感知,文中提出了一种针对有向网络表示学习的优化方法。该方法借助TrueSkill方法获取节点的层级信息,将该信息转化为边权重并引入表示学习过程。文中将此方法应用到已有的多种有向网络表示学习方法中,多个有向网络数据集上的链接预测和节点分类任务的实验结果表明,所提方法的性能相比原有方法得到了明显提升。

Directed Network Representation Method Based on Hierarchical Structure Information

Network embedding aims to embed each vertex into a low dimensional vector space and preserves certain structural relationships among the vertices in the networks.However,in the directed networks,vertexes can be reached by each other if they are in the same circle,which damages asymmetric transitivity preservation and makes representation learning model hard to capture global information of complex directed networks.This paper proposes an improved representation learning model for directed networks,which weakens the influence of circles in representation learning and enhances the ability of model to obtain global structure information.The proposed method uses TrueSkill to inference hierarchy of a directed graph and compute weight of each edge using hierarchy information.At last,this paper applies this method to some existing embedding models,and then conducts experiments on tasks of link prediction and node classification on several open source datasets.Experimental results show that the proposed method is highly scalable and effective.