2184 Local cohesion is examined through clustering coefficients, while community detection is performed using modularity optimization based on the Louvain algorithm, which allows the identification of trade communities driven by relational intensity rather than geographic proximity (Blondel et al., 2008). All network construction, metric computation, and visualization are conducted using Gephi version 0.10.1 (Aguinaga, 2024). 3. RESULTS AND STRUCTURAL ANALYSIS 3.1 Regional Overview This section evaluates the structural position of Latin America and the Caribbean (LAC) within the global copper concentrate trade network between 2010 and 2024. Beyond volumetric export dominance, the analysis considers regional participation in global weighted out-degree, brokerage capacity, and network embedding patterns (Table 1). Table 1 – Structural Indicators of LAC in the Global Copper Concentrate Trade Network (2010– 2024) Year LAC nodes LAC in Global Weighted OutDegree (%) LAC maximum betweenness centrality LAC average betweenness centrality LAC average clustering coefficient 2010 18 55% 388 41 0.193 2011 13 54% 762 120 0.084 2012 14 59% 466 60 0.026 2013 14 57% 276 53 0.025 2014 14 54% 376 65 0.038 2015 24 52% 718 41 0.124 2016 14 55% 565 91 0.031 2017 17 59% 179 29 0.022 2018 15 58% 331 78 0.013 2019 14 60% 563 73 0.026 2020 11 61% 355 96 0.006 2021 14 61% 329 48 0.120 2022 19 54% 459 48 0.013 2023 14 60% 407 43 0.016 2024 12 66% 358 54 0.012
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