Editorial: Advances in Network Data Science

This special issue features nine articles on “Advances in Network Data Science”. Data science is an interdisciplinary research field utilizing scientific methods to facilitate knowledge and insights from structured and unstructured data across a broad range of domains. Network data are proliferating in many fields, and network data analysis has become a burgeoning research in the data science community. Due to the nature of heterogeneity and complexity of network data, classical statistical approaches for network model fitting face a great deal of challenges, especially for large-scale network data. Therefore, it becomes crucial to develop advanced methodological and computational tools to cope with challenges associated with massive and complex network data analyses. This special issue highlights some recent studies in the area of network data analysis, showcasing a variety of contributions in statistical methodology, two real-world applications, a software package for network generation, and a survey on handling missing values in networks. Five articles are published in the Statistical Data Science Section. Wang and Resnick (2023) employed point processes to investigate the macroscopic growth dynamics of geographically concentrated regional networks. They discovered that during the startup phase, a self-exciting point process effectively modeled the growth process, and subsequently, the growth of links could be suitably described by a non-homogeneous Poisson process. Komolafe