The field of network analysis is rapidly evolving, with a growing focus on integrating machine learning and statistical physics methods to improve community detection and network modeling. Recent research has highlighted the importance of considering local separators and node-pair similarities in community detection, leading to more accurate and efficient algorithms. Additionally, there is a growing interest in developing application-agnostic methods for estimating video quality of experience (QoE) metrics from encrypted traffic, which has significant implications for network operators. The development of new algorithms and methodologies for network analysis, such as clique annealing and multidimensional scaling, is also underway. Noteworthy papers in this area include those that propose innovative approaches to community detection, such as CLANN, which integrates crystallization kinetics into the optimization process, and those that demonstrate the effectiveness of machine learning in estimating QoE metrics from encrypted traffic. Furthermore, research on network sampling and simplification is providing new insights into the structural characteristics of networks and the most effective methods for analyzing them.