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Albert-László Barabási is a physicist known for work on complex networks. With Réka Albert, he introduced the Barabási-Albert model, in which a growing graph attaches new vertices preferentially to vertices with larger degree.^[ref]

The central scientific idea is a mechanism, rather than merely a fitted curve: growth and degree-dependent attachment can create a broad degree distribution. This encouraged testing network formation rules against data and helped connect graph theory with statistical mechanics. The mechanism does not imply that every heavy-tailed real network follows that model.

Laboratory website: www.barabasi.com/

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/