EAGER: Efficient Methods for Characterizing Large-Scale Network Dynamics

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Many real-world phenomena can be modeled by dynamic networks whose connectivity as well as activity changes over time. Hence, there is a growing interest in elucidating the structure and dynamics of such networks. Existing approaches to this problem focus mainly on utilizing either coarse network properties or global structural features to comprehend network dynamics. Such methods often rely on extensions of network features of static networks to understand dynamic networks and fail to capture the rich dynamics of real-world networks. This exploratory project explores a hierarchical approach to decomposition of network structure and dynamics that can explain changing dynamics at multiple scales ranging from node-level to community-level. The approach is novel, and because of its untested nature, somewhat risky. The research is organized around three aims:(i) Develop information-theoretic flow based approaches that can extract multiple layers of dynamics by simultaneously optimizing for explicit community structures and partial flow dynamics in complex networks. (ii) Develop a computational framework for dynamics-aware network summarization that preserves the flow dynamics of graphs and provides a summary of the large-scale graph dynamics.The project advances the current state-of-the-art in network data analytics. The resulting tools for elucidating the structure and dynamics of complex networks at multiple scales could potentially transform the way we understand, design, engineer, and control complex networks. The project enriches research-based training and outreach activities at Wayne State University.