文献类型：期刊文献

英文题名：Multilayer Network Representation Learning Method Based on Random Walk of Multiple Information

作者：Yan, Guanghui[1];Li, Zhe[1];Luo, Hao[2];Wang, Yishu[1];Chang, Wenwen[1];Yang, Mingjie[3];Su, Rui[3];Liu, Ning[3]

第一作者：Yan, Guanghui

通信作者：Li, Z[1]

机构：[1]Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China;[2]Gansu Univ Tradit Chinese Med, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[3]State Grid Gansu Informat & Telecommun Co, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China

通信机构：[1]corresponding author), Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China.

年份：2021

卷号：9

起止页码：53178

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211510198728);Scopus(收录号：2-s2.0-85103784307);WOS:【SCI-EXPANDED(收录号:WOS:000639855100001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62062049, in part by the Natural Science Foundation of Gansu Province under Grant 20JR5RA390 and Grant 20JR10RA215, in part by the Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant 20YJCZH212, in part by the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) under Grant 202100020, and in part by the Scientific Research Foundation of the Higher Education Institutions of Education Bureau of Gansu Province under Grant 2020B-115.

语种：英文

外文关键词：Nonhomogeneous media; Licenses; Network topology; Learning systems; Feature extraction; Task analysis; Prediction algorithms; Network representation learning; multilayer network; neural network; random walk; network topology; node structure

摘要：Network representation learning aims to map nodes in the network into low-dimensional dense vectors, which can be widely used to solve the network analysis tasks. Existing methods mainly focus on single-layer homogeneous networks. However, many real-world networks consist of multiple types of nodes and edges, which are called multilayer networks. The problem of how to capture node information and use multi-type relational information is a major challenge of multilayer network representation learning. To address this problem, we propose a method of random walk of multiple information, called IFMNE, to efficiently preserve and learn node information and multi-type relational information into a unified space. This method combines node structure information with network topology information to obtain the node random walk sequence, and trains the node walk sequence on the neural network model. Experimental results are performed on five real multilayer networks, and the embedding vectors were evaluated by link prediction task. The accuracy was significantly improved on the basis of low time complexity compared with the baseline methods.