THE IMPACT OF GRAPH THEORY AND NETWORK ALGORITHMS ON AI-ENABLED DIGITAL ECOSYSTEMS

Digital ecosystems are the nervous system of modern economies, and in Iraq from 2020 to 2024 their growth depended heavily on graph theory and network algorithms. This study examined how graph structures, algorithm types, and application domains shaped outcomes such as connectivity efficiency, interaction quality, system robustness, and analytical insights under infrastructure and literacy constraints. A descriptive design using secondary data from 25 sector-year observations across telecom, logistics, e-government, education, and finance guided the analysis. Correlation results showed strong positive links between application domains and ecosystem outcomes at 0.82, graph structures at 0.79, and algorithm types at 0.74, while contextual constraints had a negative effect at −0.61. Regression confirmed application domains as the strongest driver with a coefficient of 0.38, followed by graph structures at 0.27 and algorithm types at 0.24, with constraints reducing outcomes at −0.20. The model explained 81 percent of the variance in outcomes, proving the predictive strength of mathematical drivers. Findings indicate that Iraq improved connectivity efficiency with latency reduction rising from 5 to 25 percent and reliability scores from 40 to 60, yet resilience and insights remained underdeveloped. The results imply that scaling graph-based applications can accelerate ecosystem maturity if paired with stronger infrastructure and human capacity. Recommendations target policymakers to close infrastructure gaps, managers to expand algorithm use beyond pilots, and educators to embed applied graph methods in curricula.