A Latent Space Model for Weighted Keyword Co-occurrence Networks with Applications in Knowledge Discovery in Statistics
報告人簡介
潘蕊，中央財經(jīng)大學(xué)統(tǒng)計與數(shù)學(xué)學(xué)院教授、博士生導(dǎo)師，中央財經(jīng)大學(xué)龍馬學(xué)者青年學(xué)者。主要研究領(lǐng)域為網(wǎng)絡(luò)結(jié)構(gòu)數(shù)據(jù)的統(tǒng)計建模、時空數(shù)據(jù)的統(tǒng)計分析等。在Annals of Statistics、Journal of the American Statistical Association、Journal of Business & Economic Statistics等期刊發(fā)表論文30余篇。著有中文專著《數(shù)據(jù)思維實踐》《網(wǎng)絡(luò)結(jié)構(gòu)數(shù)據(jù)分析與應(yīng)用》。主持國家自然科學(xué)基金項目、全國統(tǒng)計科學(xué)研究項目等。具有豐富的統(tǒng)計案例創(chuàng)作經(jīng)驗。曾獲得中央財經(jīng)大學(xué)青年教師教學(xué)基本功比賽二等獎，首屆中國高校財經(jīng)慕課聯(lián)盟“同課異構(gòu)”課程思政教學(xué)競賽一等獎。
內(nèi)容簡介
Keywords are widely recognized as pivotal in conveying the central idea of academic articles. In this article, we construct a weighted and dynamic keyword co-occurrence network and propose a latent space model for analyzing it. Our model has two special characteristics. First, it is applicable to weighted networks; however, most previous models were primarily designed for unweighted networks. Simply replacing the frequency of keyword co-occurrence with binary values would result in a significant loss of information. Second, our model can handle the situation where network nodes evolve over time, and assess the effect of new nodes on network connectivity. We utilize the projected gradient descent algorithm to estimate the latent positions and establish the theoretical properties of the estimators. In the real data application, we study the keyword co-occurrence network within the field of statistics. We identify popular keywords over the whole period as well as within each time period. For keyword pairs, our model provides a new way to assess the association between them. Finally, we observe that the interest of statisticians in emerging research areas has gradually grown in recent years. Supplementary materials for this article are available online.
