🤖 AI Summary
Existing approaches struggle to effectively model the social interaction structures among divers, limiting the analysis and enhancement of underwater collaboration and safety. This work introduces, for the first time, graph-based models from computational social science into diving scenarios, proposing a temporal social network framework that characterizes dynamic interactions among divers through multidimensional weighted edges defined by physical proximity, communication efficiency, and emergency support capacity. By capturing the evolving nature of diver relationships across these key dimensions, the framework establishes a scalable theoretical foundation for studying team coordination and safety in underwater environments, thereby enabling future empirical investigations and system-level optimizations.
📝 Abstract
Scuba divers form small and structured social groups that are well suited for analysis using methods from Computational Social Science (CSS). This paper proposes a conceptual graph-based framework for modeling scuba dives as social networks. Divers are represented as vertices and their interactions as edges within a temporal network. The model focuses on three dimensions of interaction: physical distance, communicative distance, and emergency distance, reflecting spatial positioning, effectiveness of underwater communication, and the ability to assist in critical situations. While the present work is conceptual and primarily descriptive, the proposed graph representation may provide a foundation for future empirical studies aimed at improving diver interaction, coordination, and safety.