Abstract
This thesis applies complex network science to analyze 35 years of global trade data and characterize the structural properties, temporal evolution, and resilience of the international trade system. Using 613,252 bilateral export observations from the United Nation (UN) Comtrade database covering 188 reporting economies from 1988 to 2022, the study constructs nine 4-year interval networks and one full-period aggregate network. Each country becomes a node, each bilateral trade relationship above a one million United States Dollars (USD) threshold becomes a weighted edge, and the resulting graphs are analyzed using small-world theory, scale-free network theory, geospatial community detection, and percolation-based resilience simulation. Nine formal hypotheses are tested. Six are confirmed, two are partially confirmed, and one (H8, SIRS risk transmission) is pending full calibration. The global trade network exhibits persistent small-world properties across all nine intervals, with small-world index σ consistently above 1.2, clustering coefficient in the range 0.82 to 0.86, and average path length between 1.38 and 1.75. The network densified substantially over the study period, from 203 nodes and 3,694 edges in 1988 to 237 nodes and over 12,000 edges from 2008 onwards. The power-law exponent evolved from α = 2.38 in 1988 to α = 2.60 in 2020, indicating gradual structural shift toward less extreme hub dominance. Louvain community detection identifies three geographically coherent trade blocs - Asia-Pacific, European, and North American, with a Pearson correlation of 0.72 between geographic proximity and bilateral clustering confirming that geography drives trade network topology. Percolation analysis reveals asymmetric resilience. The network tolerates 60 to 75 per cent random node removal before fragmenting but collapses at only 15 to 20 per cent targeted hub removal, with the largest connected component dropping from 98 per cent to 22 per cent. Cascade simulation under a severity 0.8 shock to China produces an 80 per cent immediate trade loss and a 40-step recovery trajectory under active rerouting. These findings identify the 15 to 20 per cent hub removal threshold as a critical structural vulnerability and provide empirical grounding for supply chain policies around regional diversification, strategic inventory, and backup hub strategies for semiconductors, energy, and pharmaceuticals. The thesis establishes the largest longitudinal trade network dataset yet analyzed at this methodological depth, with all code and data publicly released for reproducibility.