Publications
An information-theoretic, all-scales approach to comparing networks
Preprint, 2018
Status: Published
Citations:
Cite: [bibtex]

Abstract: As network research becomes more sophisticated, it is more common than ever for researchers to
find themselves not studying a single network but needing to analyze sets of networks. An important task when
working with sets of networks is network comparison, developing a similarity or distance measure between
networks so that meaningful comparisons can be drawn. The best means to accomplish this task remains an
open area of research. Here we introduce a new measure to compare networks, the Portrait Divergence, that
is mathematically principled, incorporates the topological characteristics of networks at all structural scales,
and is general-purpose and applicable to all types of networks. An important feature of our measure that
enables many of its useful properties is that it is based on a graph invariant, the network portrait. We test our
measure on both synthetic graphs and real world networks taken from protein interaction data, neuroscience,
and computational social science applications. The Portrait Divergence reveals important characteristics of
multilayer and temporal networks extracted from data.
[edit database entry]

Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Continuous Self-Modeling. Science 314, 1118 (2006). [Journal Page]

Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.

Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.

Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).