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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.
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Science of science
Santo Fortunato,Carl T. Bergstrom,Katy Börner,James A. Evans,Dirk Helbing,Staša Milojević,Alexander M. Petersen,Filippo Radicchi,Roberta Sinatra,Roberta Sinatra,Brian Uzzi,Alessandro Vespignani,Alessandro Vespignani,Ludo Waltman,Dashun Wang,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +17 more
TL;DR: The Science of Science (SciSci) as discussed by the authors provides a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales, providing insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science.
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Functional and topological characterization of protein interaction networks
TL;DR: This work compares four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large‐scale properties and the impact of the proteins' function and cellular localization on the network topology.
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Halting viruses in scale-free networks.
TL;DR: It is demonstrated that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate a virus.
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A Dynamic Network Approach for the Study of Human Phenotypes
César A. Hidalgo,Nicholas Blumm,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási,Nicholas A. Christakis +5 more
TL;DR: A new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN) is introduced, offering the potential to enhance the understanding of the origin and evolution of human diseases.
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Measuring preferential attachment in evolving networks
TL;DR: In this paper, the authors show that the rate at which nodes acquire links depends on the node's degree, offering direct quantitative support for the presence of preferential attachment, which is a key ingredient of many current models proposed to capture the topological evolution of complex networks.