Heterogeneous-attributes enhancement deep framework for network embedding  

作　　者：Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN 

机构地区：[1]Anhui Province Key Laboratory of Big Data Analysis and Application,University of Science and Technology of China,Hefei 230022,China [2]National Key Laboratory of Blind Signal Processing,Chengdu 610041,China [3]School of Computer Science and Technology,University of Science and Technology of China,Hefei 230022,China

出　　处：《Frontiers of Computer Science》2021年第6期121-131,共11页中国计算机科学前沿（英文版）

基　　金：This research was partially supported by the National Natural Science Foundation of China(Grants Nos.U1605251 and 61727809).

摘　　要：Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关 键 词：network embedding heterogeneous-attributes deep framework inconsistent 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]