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 cooccurrence 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.
嘉宾介绍
张妍，厦门大学经济学院统计学与数据科学系博士研究生，师从方匡南教授和潘蕊教授。研究方向为网络结构数据分析、模型平均。科研成果发表在Journal of Computational and Graphical Statistics、Knowledge-Based Systems等期刊，参加编著《网络结构数据分析与应用》。
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