Institution
Santa Fe Institute
Nonprofit•Santa 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 & Context (language use). The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: This article conducted an experiment to see if players were conformists by separating individual and social learners and found that a subset of social learners behaved according to a classic model of conformity, while the remaining social learners did not respond to frequency information.
255 citations
••
TL;DR: An unsupervised method is introduced to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity.
Abstract: Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved.
255 citations
••
TL;DR: It is conjecture that the small world pattern arises from the compact design in which many elements share a small, close physical neighborhood plus the fact that the system must define a single connected component (which requires shortcuts connecting different integrated clusters).
Abstract: Recent theoretical studies and extensive data analyses have revealed a common feature displayed by biological, social, and technological networks: the presence of small world patterns. Here we analyze this problem by using several graphs obtained from one of the most common technological systems: electronic circuits. It is shown that both analogic and digital circuits exhibit small world behavior. We conjecture that the small world pattern arises from the compact design in which many elements share a small, close physical neighborhood plus the fact that the system must define a single connected component (which requires shortcuts connecting different integrated clusters). The degree distributions displayed are consistent with a conjecture concerning the sharp cutoffs associated to the presence of costly connections [Amaral et al., Proc. Natl. Acad. Sci. USA 97, 11 149 (2000)], thus providing a limit case for the classes of universality of small world patterns from real, artificial networks. The consequences for circuit design are outlined.
254 citations
••
TL;DR: A mathematical model of a tripartite structure of users, resources, and tags-labels collaboratively applied by the users to the resources in order to impart meaningful structure on an otherwise undifferentiated database is proposed.
Abstract: In the last few years we have witnessed the emergence, primarily in online communities, of new types of social networks that require for their representation more complex graph structures than have been employed in the past. One example is the folksonomy, a tripartite structure of users, resources, and tags—labels collaboratively applied by the users to the resources in order to impart meaningful structure on an otherwise undifferentiated database. Here we propose a mathematical model of such tripartite structures that represents them as random hypergraphs. We show that it is possible to calculate many properties of this model exactly in the limit of large network size and we compare the results against observations of a real folksonomy, that of the online photography website Flickr. We show that in some cases the model matches the properties of the observed network well, while in others there are significant differences, which we find to be attributable to the practice of multiple tagging, i.e., the application by a single user of many tags to one resource or one tag to many resources
254 citations
••
TL;DR: This work compares levels of secondary extinctions in communities generated by four structural food-web models and a fifth null model in response to sequential primary species removals and finds increased robustness and decreased levels of web collapse are associated with increased diversity and increased complexity.
Abstract: Species loss in ecosystems can lead to secondary extinctions as a result of consumerresource relationships and other species interactions. We compare levels of secondary extinctions in communities ...
254 citations
Authors
Showing all 606 results
Name | H-index | Papers | Citations |
---|---|---|---|
James Hone | 127 | 637 | 108193 |
James H. Brown | 125 | 423 | 72040 |
Alan S. Perelson | 118 | 632 | 66767 |
Mark Newman | 117 | 348 | 168598 |
Bette T. Korber | 117 | 392 | 49526 |
Marten Scheffer | 111 | 350 | 73789 |
Peter F. Stadler | 103 | 901 | 56813 |
Sanjay Jain | 103 | 881 | 46880 |
Henrik Jeldtoft Jensen | 102 | 1286 | 48138 |
Dirk Helbing | 101 | 642 | 56810 |
Oliver G. Pybus | 100 | 447 | 45313 |
Andrew P. Dobson | 98 | 322 | 44211 |
Carel P. van Schaik | 94 | 329 | 26908 |
Seth Lloyd | 92 | 490 | 50159 |
Andrew W. Lo | 85 | 378 | 51440 |