13,173 research outputs found
A ThDP-dependent enzymatic carboligation reaction involved in Neocarazostatin A tricyclic carbazole formation
Acknowledgements This work was supported by grants from the National Natural Science Foundation of China (31570033 to Y. Y.) and the Leverhulme Trust-Royal Society Africa Award (AA090088 to K. K and H. D.). Open access via RSC Gold 4 Gold.Peer reviewedPublisher PD
Critical behavior of a stochastic anisotropic Bak-Sneppen model
In this paper we present our study on the critical behavior of a stochastic
anisotropic Bak-Sneppen (saBS) model, in which a parameter is
introduced to describe the interaction strength among nearest species. We
estimate the threshold fitness and the critical exponent by
numerically integrating a master equation for the distribution of avalanche
spatial sizes. Other critical exponents are then evaluated from previously
known scaling relations. The numerical results are in good agreement with the
counterparts yielded by the Monte Carlo simulations. Our results indicate that
all saBS models with nonzero interaction strength exhibit self-organized
criticality, and fall into the same universality class, by sharing the
universal critical exponents.Comment: 9 pages, 7 figures. arXiv admin note: text overlap with
arXiv:cond-mat/9803068 by other author
Community detection by label propagation with compression of flow
The label propagation algorithm (LPA) has been proved to be a fast and
effective method for detecting communities in large complex networks. However,
its performance is subject to the non-stable and trivial solutions of the
problem. In this paper, we propose a modified label propagation algorithm LPAf
to efficiently detect community structures in networks. Instead of the majority
voting rule of the basic LPA, LPAf updates the label of a node by considering
the compression of a description of random walks on a network. A multi-step
greedy agglomerative strategy is employed to enable LPAf to escape the local
optimum. Furthermore, an incomplete update condition is also adopted to speed
up the convergence. Experimental results on both synthetic and real-world
networks confirm the effectiveness of our algorithm
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