Network Analysis of Medical Legal Citation (AI and Graphs)

Complex Network Analysis of Legal Citation Networks (esp. medical) in South Korea (1951–2023)

We analyze approximately 86,000 legal cases from South Korea (1951–2023) by constructing (complex) citation networks, modeling each case as a node and citation relationships as edges. Through centrality/hub analysis, we identify key hub cases. We extract and analyze a focused subnetwork comprising medical-related cases.

Segmenting the network along presidential cycles reveals that the distribution of medical hub cases changes over time. Cases related to criminal liability in medical accidents were prominent hubs from the late 1990s to early 2000s, whereas subsequent administrations saw civil rulings on patients’ rights and institutional responsibilities become new hubs. This analysis illustrates structural reorganization within the network.

From an optimization perspective, we apply the minimum dominating set (MDS) approach, supplemented by AI-assisted heuristics, to extract a minimal influential subset within the medical subnetwork. Complexity analysis shows power-law degree distributions with the exponent approximately 2.1, indicating scale-free characteristics. An AI-assisted optimization, balancing sparsification and policy relevance, facilitates a more targeted identification of legally and policy-relevant cases.

We suggest quantitative investigations into the structural features and dynamic evolution of legal citation networks in the medical domain. These provide practical implications for legal and policy decision-making in healthcare governance.