Developed a sophisticated network analysis system for S&P 500 stocks (2018-2022), employing advanced graph theory and machine learning techniques to visualize and analyze market interconnectivity patterns. The project revealed crucial insights into stock market behavior during the COVID-19 pandemic, identifying influential stocks and sector-based communities through correlation analysis and network metrics. Notable findings included the detection of scale-free properties in market networks and the quantification of sector-based trading patterns using Jaccard similarity coefficients.

Developed a sophisticated link prediction system for actor co-occurrence networks using advanced machine learning techniques, achieving 76.42% prediction accuracy with logistic regression. The project successfully combined graph structural features with node attributes to predict missing edges in the network, demonstrating superior performance across multiple evaluation metrics including F1 score (0.7607) and AUC score.