The field of network analysis and modeling is rapidly advancing, with a focus on developing new methods and techniques to understand and represent complex systems. Recently, there has been a surge of interest in using machine learning and probabilistic approaches to analyze and model networks, particularly in the context of large-scale, higher-order networks. These approaches have shown promise in improving our understanding of network structure and behavior, and in identifying key patterns and relationships that can inform decision-making and policy development. Notable papers in this area include the work on supervised link prediction in co-authorship networks, which demonstrates the effectiveness of integrating research interest and affiliation similarity into link prediction models. The paper on broad spectrum structure discovery in large-scale higher-order networks also stands out, as it introduces a new class of probabilistic models that can efficiently represent and discover mesoscale structure in hypergraphs. Additionally, the work on recovering fairness directly from modularity presents a novel approach to fair community partitioning, which has significant implications for ensuring that network analysis and modeling techniques are equitable and just.