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Institution

Santa Fe Institute

NonprofitSanta Fe, New Mexico, United States
About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.


Papers
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Journal ArticleDOI
11 Aug 2006-Science
TL;DR: Contrary to Coyne's assertions, this paper did not advocate a macromutational innovation of phyla but considered the consequences of the introduction of developmental constraints for the evolution of gene Regulatory networks based on recent empirical studies of gene regulatory networks.
Abstract: Contrary to Coyne's assertions, our paper did not advocate a macromutational innovation of phyla but considered the consequences of the introduction of developmental constraints for the evolution of gene regulatory networks based on recent empirical studies of gene regulatory networks.

155 citations

Journal ArticleDOI
TL;DR: Spatial distribution systems for energy, fuel, medical, and food supply are studied and it is found that these systems show power-law scaling as well, when the number of "supply stations" is plotted over the population size.
Abstract: In previous work, it has been proposed that urban structures may be understood as a result of self-organization principles. In particular, researchers have identified fractal structures of public transportation networks and land use patterns. Here, we will study spatial distribution systems for energy, fuel, medical, and food supply. It is found that these systems show power-law scaling as well, when the number of “supply stations” is plotted over the population size. Surprisingly, only some supply systems display a linear scaling with population size. Others show sublinear or superlinear scaling. We suggest an interpretation regarding the kind of scaling law that is expected in dependence of the function and constraints of the respective supply system.

155 citations

Book ChapterDOI
01 Oct 2003
TL;DR: In this paper, the authors used an evolutionary algorithm involving minimization of link density and average distance to find the topology of complex networks, including sparse exponential-like networks, sparse scale-free networks, star networks, and highly dense networks.
Abstract: Many complex systems can be described in terms of networks of interacting units. Recent studies have shown that a wide class of both natural and artificial nets display a surprisingly widespread feature: the presence of highly heterogeneous distributions of links, providing an extraordinary source of robustness against perturbations. Although most theories concerning the origin of these topologies use growing graphs, here we show that a simple optimization process can also account for the observed regularities displayed by most complex nets. Using an evolutionary algorithm involving minimization of link density and average distance, four major types of networks are encountered: (a) sparse exponential-like networks, (b) sparse scale-free networks, (c) star networks and (d) highly dense networks, apparently defining three major phases. These constraints provide a new explanation for scaling of exponent about -3. The evolutionary consequences of these results are outlined.

155 citations

Journal ArticleDOI
TL;DR: This work introduces a unique avenue toward El Niño prediction based on network methods, inspecting emerging teleconnections and can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the early-warning period.
Abstract: Although anomalous episodic warming of the eastern equatorial Pacific, dubbed El Nino by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead. A significant extension of the prewarning time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a unique avenue toward El Nino prediction based on network methods, inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode—linking the El Nino basin (equatorial Pacific corridor) and the rest of the ocean—builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data available since 1950 and yields hit rates above 0.5, whereas false-alarm rates are below 0.1.

155 citations

Journal ArticleDOI
TL;DR: It is shown that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimumspanning Tree and the average reliability of links is slightly greater than the average unreliable links in the average linkage minimum Spanning tree.
Abstract: We introduce a new technique to associate a spanning tree to the average linkage cluster analysis. We term this tree as the Average Linkage Minimum Spanning Tree. We also introduce a technique to associate a value of reliability to the links of correlation-based graphs by using bootstrap replicas of data. Both techniques are applied to the portfolio of the 300 most capitalized stocks traded on the New York Stock Exchange during the time period 2001–2003. We show that the Average Linkage Minimum Spanning Tree recognizes economic sectors and sub-sectors as communities in the network slightly better than the Minimum Spanning Tree. We also show that the average reliability of links in the Minimum Spanning Tree is slightly greater than the average reliability of links in the Average Linkage Minimum Spanning Tree.

155 citations


Authors

Showing all 606 results

NameH-indexPapersCitations
James Hone127637108193
James H. Brown12542372040
Alan S. Perelson11863266767
Mark Newman117348168598
Bette T. Korber11739249526
Marten Scheffer11135073789
Peter F. Stadler10390156813
Sanjay Jain10388146880
Henrik Jeldtoft Jensen102128648138
Dirk Helbing10164256810
Oliver G. Pybus10044745313
Andrew P. Dobson9832244211
Carel P. van Schaik9432926908
Seth Lloyd9249050159
Andrew W. Lo8537851440
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202341
202241
2021297
2020309
2019263
2018231