A Latent Space Model for Weighted Keyword Co-occurrence Networks with Applications in Knowledge Discovery in Statistics

<b>Keywords</b> are widely recognized as pivotal in conveying the central idea of academic articles. In this article, we construct a weighted and dynamic <b>keyword</b> 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 <b>keyword</b> 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 <b>keyword</b> co-occurrence network within the field of statistics. We identify popular <b>keywords</b> over the whole period as well as within each time period. For <b>keyword</b> 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