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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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Dynamics of complex systems: scaling laws for the period of boolean networks

TL;DR: A method to calculate the period of a finite Boolean system, by identifying the mechanisms determining its value, can be applied to systems of arbitrary topology, and can serve as a roadmap for understanding the dynamics of large interacting systems in general.
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Self-assembled island formation in heteroepitaxial growth

TL;DR: In this article, the authors investigate island formation during heteroepitaxial growth using an atomistic model that incorporates deposition, activated diffusion, and stress relaxation, and indicate the existence of a strain assisted kinetic mechanism responsible for the self-assembling process.
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Lung tissue viscoelasticity: a mathematical framework and its molecular basis

TL;DR: It is shown that replacing ordinary time derivatives with fractional time derivatives in the constitutive equation of conventional spring-dashpot systems naturally leads to power law relaxation function, the Fourier transform of which is the constant-phase impedance with alpha = 1 - beta.
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Physics of the rhythmic applause

TL;DR: The results demonstrate that while this process shares many characteristics of other systems that are known to synchronize, it also has features that are unexpected and unaccounted for in many other systems.
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Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

TL;DR: It is found that 21% of the proteins in the PPI network are indispensable, Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states.