지식그래프와 인공지능 기반 과학적 정보분석 기법 연구 : 테러·초국가 범죄 네트워크를 중심으로

초록

Terrorist organizations and transnational criminal networks represent major national security threats that operate through complex relational structures. Effectively identifying and tracking their organizational structures and key actors requires advanced intelligence analysis methodologies. In particular, covert networks are characterized by intentional secrecy and incomplete information environments, which impose significant limitations on traditional statistical or actor-centered approaches in capturing their structural characteristics. To address these challenges, network analysis methods grounded in graph theory and social network analysis have emerged as important analytical tools for structurally modeling and quantitatively analyzing relationships among individuals, organizations, and events. Recent advances in machine learning and deep learning have further expanded the analytical capabilities of network research. Graph embedding and representation learning techniques enable ...

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