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]
Emergence of scaling in random networks by Albert-László Barabási and Réka Albert. 1999, growth and preferential-attachment mechanism
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.
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Albert-László Barabási is a prominent Hungarian-American physicist known for his work in network science. He is particularly renowned for his research on complex networks, which has applications in various fields including sociology, biology, and computer science. Barabási is best known for the development of the Barabási-Albert model, which describes how networks grow and evolve over time, emphasizing the role of "preferential attachment" where nodes with higher connectivity are more likely to attract new connections.