L
Lidia A. Braunstein
Researcher at National University of Mar del Plata
Publications - 143
Citations - 4042
Lidia A. Braunstein is an academic researcher from National University of Mar del Plata. The author has contributed to research in topics: Interdependent networks & Complex network. The author has an hindex of 31, co-authored 139 publications receiving 3552 citations. Previous affiliations of Lidia A. Braunstein include National Scientific and Technical Research Council & Boston University.
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Unification of theoretical approaches for epidemic spreading on complex networks.
TL;DR: This short survey unifies the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean- field, the quench mean-fields, dynamical message-passing, link percolation, and pairwise approximation.
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Optimal Paths in Disordered Complex Networks
TL;DR: For strong disorder, where the maximal weight along the path dominates the sum, l(opt) approximately N(1/3) in both Erdos-Rényi (ER) and Watts-Strogatz (WS) networks.
Journal ArticleDOI
Unification of theoretical approaches for epidemic spreading on complex networks
TL;DR: In this article, the authors unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean field, the heterogeneous mean-field, the quench meanfield, dynamical message-passing, link percolation, and pairwise approximation.
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Transport in weighted networks: partition into superhighways and roads.
TL;DR: It is found that one can improve significantly the global transport by improving a tiny fraction of the network, the superhighways, by ensuring the distribution of the centrality for the infinite incipient percolation cluster satisfies a power law.
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Suppressing disease spreading by using information diffusion on multiplex networks.
Wei Wang,Wei Wang,Quan-Hui Liu,Shi-Min Cai,Ming Tang,Lidia A. Braunstein,Lidia A. Braunstein,H. Eugene Stanley +7 more
TL;DR: In this article, the authors investigated the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network and found that there is an optimal information transmission rate that markedly suppresses the disease spreading.