A
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
More filters
Journal ArticleDOI
Maximum angle of stability in wet and dry spherical granular media
TL;DR: In this paper, stability criteria can be used to calculate the maximum angle of stability of a granular medium composed of spherical particles in three dimensions and circular disks in two dimensions, and the predicted angles are in good agreement with the experimental results.
Journal ArticleDOI
Bioinformatics Analysis of Experimentally Determined Protein Complexes in the Yeast Saccharomyces cerevisiae
TL;DR: It is demonstrated quantitatively that protein complexes in the yeast Saccharomyces cerevisiae are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or nonessential), and share identical functional classification and cellular localization.
Journal ArticleDOI
Granular drag on a discrete object: shape effects on jamming.
TL;DR: A deviation above the expected linear depth dependence is observed, and the magnitude of the deviation is apparently controlled by geometrical factors.
Journal ArticleDOI
Distribution of node characteristics in complex networks.
TL;DR: It is shown that when nodes in a network belong to two distinct classes, two independent parameters are needed to capture the detailed interplay between the network structure and node properties, and it is found that thenetwork structure significantly limits the values of these parameters.
Proceedings ArticleDOI
Information spreading in context
TL;DR: In this article, the authors combine two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system, and find that the social and organizational context significantly impacts to whom and how fast people forward information.