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Network science studies systems represented by graphs, combining structural measurements, data analysis and models of network formation or dynamics. Vertices represent entities and edges represent a specified relation; directed graphs, weighted graphs and temporal data encode different kinds of interaction.

A transportation network might assign stations to vertices and direct services to edges. Vertex degree counts immediate connections, while shortest-path graph distances measure routes through several stations. These answer different questions: a high-degree station need not minimize all journey times, especially when edge weights represent duration.

Useful models must connect the graph definition to the measured system. A biological network built from physical binding experiments differs from one built from statistical correlation. Comparing an observed statistic with a suitable random graph model helps distinguish a structural pattern from what the vertex and edge counts already imply.

Réka Albert is a physicist whose work connects network science with biological networks. With Albert-László Barabási, she coauthored the 1999 paper introducing a growing preferential attachment model for the emergence of broad degree distributions, now called the Barabási-Albert model.^[ref]

Her biological work uses logical models to connect the structure of a regulatory graph with its possible dynamical states. Such models represent interactions without requiring a measured kinetic constant for every reaction. Her institutional profile describes research on mechanisms controlling complex-network dynamics and on predictive modeling of biological systems.^[ref]

Homepage: www.ralbert.me/