Aims and scope Social Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers, educators, policymakers, professionals, and everyone interested in the data-driven network domain from the academia, the public sector, the private sector, and the general public. SNAM is the main venue for a wide range of researchers, practitioners and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management, and political sciences. We solicit practical, experimental and theoretical works on data-driven network analysis and mining using a wide range of techniques from social sciences, mathematics, statistics, physics, network science, data science and computer science. The main areas covered by SNAM include, but not limited to: (1) data mining, machine learning, deep learning, large language models and artificial intelligence advances on the discovery and analysis of communities, personalization for solitary activities (e.g. search) and social activities (e.g. discovery of potential friends), the analysis of user behavior in open forums (e.g. conventional sites, blogs and forums) and in commercial platforms (e.g. e-auctions), and the associated ethics, legal issues, security and privacy-preservation challenges; (2) social network modeling, simulation, and analysis, as well as the understanding of social systems from the quantitative and/or computational perspective (3) the construction of scalable and customizable social networking infrastructure, the identification and discovery of complex, dynamic growth, and evolution patterns using machine/deep learning, LLMs, and data mining approaches or multi-agent-based simulation (4) social network analysis and mining for open-source intelligence, cybersecurity, and homeland security. Submissions should elaborate on data mining and machine/deep learning, LLMs or related methods, issues associated to data preparation and pattern interpretation, both for multi-source conventional data (usage logs, query logs, document collections) and multimedia data (pictures and videos with their annotations, multi-channel usage data, etc.). Topics covered by SNAM include, but are not limited to: Applications of social network in business engineering, scientific and medical domains, homeland security, terrorism and criminology, fraud detection, public sector, politics, and case studies Anomaly and outlier detection in social media and networks Augmented reality, virtual reality and gaming in social networking and social media Behavior and identity detection and monitoring Community discovery in large-scale and complex social networks Contextual social network analysis and modeling of socio-cultural and behavioral aspects Data models and query models for social media and networks Data collection and preparation for social network analysis and mining Dynamics and evolution patterns of social networks Evolution of communities in the Web information acquisition and establishment of social relations Generative AI in social networks and social media Large language models and large-scale graph algorithms Link and node prediction, and trend prediction in social networks Misinformation, fake news, spam, and misbehaviour detection in communities Mobile and stream data analysis for social network applications Multi-agent based social network modeling and analysis Multidisciplinary applications of social network analysis Network integration and conflict resolution Online social networking and human computer interaction Pattern presentation for end-users and experts Personalization for search and for social interaction Recommendations for e-commerce and business applications Recommendation networks Search algorithms for social networks Privacy issues, ethical considerations, legal challenges, cybersecurity, cyberbullying and harassment in social networking and social media Social media monitoring and analysis Spatio-temporal aspects in social networks and social media Tools and infrastructures for social networking platforms Web 2.0 and Web communities Trustworthy social media and networks, and data protection inside communities.