2026-09-18T20:52:58Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1208282026-05-15T17:54:03Zcom_1813_35col_1813_47
Papachristou, Marios
Kleinberg, Jon
Pierson, Emma
Banerjee, Siddhartha
2026-04-02T18:50:34Z
2025-08
ProQuest Submission ID: 15071
ProQuest Publication ID: 32120004
https://hdl.handle.net/1813/120828
https://doi.org/10.7298/rm2m-az62
17422395
524 pages
The resilience of interconnected systems, such as financial networks, supply chains, software systems, and social networks, is a critical concern in today's highly connected world. While interconnectedness enables efficient economic transactions, rapid social learning, and adaptability to shocks, it also increases vulnerability to systemic risks, where localized disruptions can propagate and cause widespread failures. This thesis addresses this fundamental challenge by examining how to reason about and reinforce the resilience of complex networks through theoretical and applied tools, including probability, statistics, algorithms, and network science, whereas we rely on centralized and decentralized decision-making to design interventions that mitigate cascading failures and bolster network stability. First, the thesis focuses on optimizing resource allocation in networks undergoing contagion, developing novel resilience metrics for supply chains, and creating efficient algorithms to prevent cascading failures. Secondly, this thesis studies models of contagion and gives a formalized definition of resilience. Then, this thesis explores decentralized privacy-aware decision-making to reconcile privacy with efficient social learning in risk-prone environments to ensure resilience. Finally, the thesis studies models of network formation and also suggest modern ways to view complex interconnected systems through the lens of LLMs.
2027-09-09
en
contagion
interventions
LLMs
networks
privacy
resilience
From Contagion to Stability: Insights into Network Dynamics, Resilience and Stability
dissertation or thesis
application/pdf
Computer Science
Cornell University
Doctor of Philosophy
Ph. D., Computer Science
https://hdl.handle.net/1813/59810.2
Publication
Cornell Theses and Dissertations
Papachristou, Marios
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
embargo
embargo
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<Abstract>The resilience of interconnected systems, such as financial networks, supply chains, software systems, and social networks, is a critical concern in today's highly connected world. While interconnectedness enables efficient economic transactions, rapid social learning, and adaptability to shocks, it also increases vulnerability to systemic risks, where localized disruptions can propagate and cause widespread failures. This thesis addresses this fundamental challenge by examining how to reason about and reinforce the resilience of complex networks through theoretical and applied tools, including probability, statistics, algorithms, and network science, whereas we rely on centralized and decentralized decision-making to design interventions that mitigate cascading failures and bolster network stability. First, the thesis focuses on optimizing resource allocation in networks undergoing contagion, developing novel resilience metrics for supply chains, and creating efficient algorithms to prevent cascading failures. Secondly, this thesis studies models of contagion and gives a formalized definition of resilience. Then, this thesis explores decentralized privacy-aware decision-making to reconcile privacy with efficient social learning in risk-prone environments to ensure resilience. Finally, the thesis studies models of network formation and also suggest modern ways to view complex interconnected systems through the lens of LLMs.</Abstract>
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