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Albert-László Barabási
Researcher at Northeastern University
Publications - 463
Citations - 217721
Albert-László Barabási is an academic researcher from Northeastern University. The author has contributed to research in topics: Complex network & Network science. The author has an hindex of 152, co-authored 438 publications receiving 200119 citations. Previous affiliations of Albert-László Barabási include Budapest University of Technology and Economics & Lawrence Livermore National Laboratory.
Papers
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Journal ArticleDOI
Avalanches and the directed percolation depinning model: Experiments, simulations, and theory.
Luís A. Nunes Amaral,Luís A. Nunes Amaral,Albert-László Barabási,Albert-László Barabási,Sergey V. Buldyrev,Sergey V. Buldyrev,S. T. Harrington,S. T. Harrington,Shlomo Havlin,Shlomo Havlin,Reza Sadr-Lahijany,Reza Sadr-Lahijany,H. E. Stanley,H. E. Stanley +13 more
TL;DR: The scaling properties of the avalanches in the DPD model are related to the scaling properties for the self-organized depinning (SOD) model, a variant of the D PD model, and good agreement is found between experimental, theoretical and numerical approaches.
Journal ArticleDOI
Multifractality of growing surfaces.
Albert-László Barabási,Roch Bourbonnais,Mogens H. Jensen,János Kertész,Tamás Vicsek,Yi-Cheng Zhang +5 more
TL;DR: Large-scale computer simulations of experimentally motivated (1+1)-dimensional models of kinetic surface roughening with power-law-distributed amplitudes of uncorrelated noise show strong multifractal scaling behavior up to a crossover length depending on the system size.
Book ChapterDOI
Hierarchical Organization of Modularity in Complex Networks
TL;DR: This work focuses on the metabolic network of 43 distinct organisms and shows that many small, highly connected topologic modules combine in a hierarchical manner into larger, less cohesive units, their number and degree of clustering following a power law.
Posted Content
Evolutionary conservation of motif constituents within the yeast protein interaction network
TL;DR: In this paper, the authors demonstrate that proteins organized in cohesive patterns of interactions are conserved to a significantly higher degree than those that do not participate in such motifs, indicating that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate.
Journal ArticleDOI
A Genetic Model of the Connectome
TL;DR: The proposed connectome model offers a self-consistent framework to link the genetics of an organism to the reproducible architecture of its connectome, offering experimentally falsifiable predictions on the genetic factors that drive the formation of individual neuronal circuits.