Advances in Network Ana lysis and Optimization
Networks are everywhere, from the finan cial systems powering global economies to the social connections that shape communities and the infrastructure that moves people and goods . This dissertation examines the integration of network science, oper ations research, and machine learning to develop methodological contr ibutions that advance the modeling, optimization, and interpretation of complex systems. These contributions are presented across four cha pters: • Chapter 2 introduces the Feature-Based Network Constructio n (FBNC) framework for reconstructing networks from partial or aggreg ate data, enabling exact feature-constrained sampling and providing n ew tools for both analysis and “what-if” scenario exploration. � � Chapter 3 investigates network connectivity optimization through th e lens of Markov chains, proposing an algorithm that directly minimiz es mean first passage times while remaining robust to uncertainty in edge presence. • Chapter 4 presents CoNNect, a connectivity-preserv ing regularization method that enforces computational efficiency thro ugh sparsity in neural networks without sacrificing expressiveness or performance. • Chapter 5 develops a clustering framework to identi fy the functional positions of financial institutions within multi-la yer financial networks, offering regulators interpretable insights in to systemic roles such as intermediaries, connectors, and peripheral actors.
More information on the thesis
DESCRIPTION: Networks are everywhere, from the financial systems power ing global economies to the social connections that shape communities and the infrastructure that moves people and goods. This dissertatio n examines the integration of network science, operations research, a nd machine learning to develop methodological contributions that adva nce the modeling, optimization, and interpretation of complex systems . These contributions are presented across four chapters: • Chapter 2 introduces the Feature-Based Network Construction (FBNC) framework for reconstructing networks from partial or aggregate data, enabling exact feature-constrained sampling and providing new tools for both analysis and “what-if” scenario exploration. • Chapter 3 invest igates network connectivity optimization through the lens of Markov c hains, proposing an algorithm that directly minimizes mean first pass age times while remaining robust to uncertainty in edge presence. • Chapter 4 presents CoNNect, a connectivity-preserving regularization method that enforces computational efficiency through sparsity in ne ural networks without sacrificing expressiveness or performance. • Chapter 5 develops a clustering framework to identify the functional positions of financial institutions within multi-layer financial netw orks, offering regulators interpretable insights into systemic roles such as intermediaries, connectors, and peripheral actors. More infor mation on the thesis Advances in Network Analysis and Optimiz ation END:VEVENT END:VCALENDAR