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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
The fundamental advantages of temporal networks
Aming Li,Aming Li,Sean P. Cornelius,Sean P. Cornelius,Yang-Yu Liu,Yang-Yu Liu,Long Wang,Albert-László Barabási +7 more
TL;DR: It is demonstrated that temporal networks can be controlled more efficiently and require less energy than their static counterparts, and have control trajectories that are considerably more compact than those characterizing static networks.
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Universality in network dynamics
Baruch Barzel,Baruch Barzel,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +4 more
TL;DR: A self-consistent theory of dynamical perturbations in complex systems is developed, allowing us to systematically separate the contribution of the network topology and dynamics.
Journal Article
Targeted Control of Complex Networks
TL;DR: An alternate ‘k-walk’ theory for directed tree networks is developed, and it is rigorously proved that one node can control a set of target nodes if the path length to each target node is unique.
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Erratum: Universal resilience patterns in complex networks
TL;DR: This corrects the article to show that the method used to derive the H2O2 “spatially aggregating force” is based on a two-step process, not a single step, like in the case of H1N1.
Journal ArticleDOI
Control Centrality and Hierarchical Structure in Complex Networks
Yang-Yu Liu,Jean-Jacques E. Slotine,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +4 more
TL;DR: In this paper, the authors introduced the concept of control centrality to quantify the ability of a single node to control a directed weighted network and showed that it is mainly determined by the network's degree distribution.