Representation learning on textual network with personalized Page Rank  

作　　者：Teng LI Yong DOU 

机构地区：[1]National Laboratory for Parallel and Distributed Processing,National University of Defense Technology,Changsha 410073,China

出　　处：《Science China(Information Sciences)》2021年第11期91-100,共10页中国科学（信息科学）（英文版）

基　　金：supported by National Science and Technology Major Projects on Core Electronic Devices,High-End Generic Chips and Basic Software(Grant No.2018ZX01028101);National Natural Science Foundation of China(Grant No.61732018)。

摘　　要：Representation learning on textual network or textual network embedding, which leverages rich textual information associated with the network structure to learn low-dimensional embedding of vertices, has been useful in a variety of tasks. However, most approaches learn textual network embedding by using direct neighbors. In this paper, we employ a powerful and spatially localized operation: personalized Page Rank(PPR) to eliminate the restriction of using only the direct connection relationship. Also, we analyze the relationship between PPR and spectral-domain theory, which provides insight into the empirical performance boost. From the experiment, we discovered that the proposed method provides a great improvement in linkprediction tasks, when compared to existing methods, achieving a new state-of-the-art on several real-world benchmark datasets.

关 键 词：representation learning network embedding Page Rank textual network personalized Page Rank 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]