An Algorithmic Framework for Assessing Stability of Dynamic Ego Networks

This thesis introduces a comprehensive algorithmic framework designed to assess the stability of dynamic ego networks, with a focus on understanding and quantifying changes in localized network structures over time. The research leverages foundational concepts from graph theory and stochastic processes, integrating models such as the Pólya urn to model the probabilistic reinforcement of network connections.