Temporal Network Analysis and Visualization

Networks in the real world are rarely static. Social connections form and dissolve, biological interactions shift with environmental conditions, and infrastructure dependencies change with load. This project develops visual analytics methods for understanding how network structure, community composition, and centrality evolve over time.

A key challenge is scalability: temporal networks can have millions of edges across thousands of time steps. We focus on summarization, progressive rendering, and user-driven exploration strategies.