The field of social network analysis is rapidly evolving, with a growing focus on developing innovative models and methods to understand the complex dynamics of human interactions. Recent research has highlighted the importance of considering spatially structured population networks, rather than relying on traditional Erdos-Renyi graphs, to better capture the spread of diseases and information. Noteworthy papers in this area include one that proposes a novel dynamic epidemic model for successive opinion diffusion in social networks, and another that introduces a regret-aware framework for effective social media advertising. The use of interaction streams, rather than networks, is also being explored as a more realistic way to study social phenomena. Additionally, research on organizational worker mobility has shown that social connections play a significant role in job changes, with workers often preferring to reunite with past co-workers. Other notable works include a study on the causal mechanism of political opinion dynamics, which conceptualizes opinion dynamics as hierarchical coarse-graining, and a paper on mitigating attention to inappropriate content based on attention dynamics models. Overall, these advances are helping to shed new light on the complex dynamics of social networks and their role in shaping our world.