Abstract: Network data analysis is a powerful tool for understanding complex relationships and interactions across various domains (Goldenberg et al., 2010). For instance, online social networks provide insights into individuals’ political orientations (Adamic and Glance, 2005); protein-protein interaction networks aid in predicting effective drug combinations (Cheng, Kovács and Barabási, 2019); co-authorship and citation networks reveal trends in academic research (Ji et al., 2021); international trade networks help analyze global trading relationships and economic movements (Anderson, 2011); and network structures also play a crucial role in estimating causal effects (Aronow and Samii, 2017). With the growing availability of large-scale networks and the expanding range of network-related applications, statistical methodology and theory for network analysis have seen significant advancements recently. This special issue brings together a collection of papers that explore timely and relevant topics in the field.