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Functional cartography of complex metabolic networks
TLDR
A methodology is proposed that can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections, which yields a ‘cartographic representation’ of complex networks.
The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
TLDR
It is found that the worldwide air transportation network is a scale-free small-world network, and it is demonstrated that the most connected cities are not necessarily the most central, resulting in anomalous values of the centrality.
Cartography of complex networks: modules and universal roles.
TLDR
It is demonstrated that one can (i) find modules in complex networks and (ii) classify nodes into universal roles according to their pattern of within- and between-module connections, which yields a 'cartographic representation' of complex networks.
Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance
TLDR
A model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations is proposed.
Self-similar community structure in a network of human interactions.
TLDR
The results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar, suggesting that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.
Missing and spurious interactions and the reconstruction of complex networks
TLDR
This work is able to reliably identify both missing and spurious interactions in noisy network observations and enables network reconstructions that yield estimates of the true network properties that are more accurate than those provided by the observations themselves.
Module identification in bipartite and directed networks.
TLDR
This work reports on an approach especially suited for module detection in bipartite networks, and defines a set of random networks that enable it to validate the approach.
Modularity from fluctuations in random graphs and complex networks.
TLDR
It is shown both numerically and analytically that random graphs and scale-free networks have modularity and it is argued that this fact must be taken into consideration to define statistically significant modularity in complex networks.
Extracting the hierarchical organization of complex systems
TLDR
An unsupervised method for extracting the hierarchical organization of complex biological, social, and technological networks is introduced and validated and an ensemble of hierarchically nested random graphs is defined, which is used to validate the method.
QUANTITATIVE PATTERNS IN THE STRUCTURE OF MODEL AND EMPIRICAL FOOD WEBS
TLDR
It is demonstrated that the quantitative unifying patterns that describe the properties of the food-web models considered earlier also describe the majority of the empirical webs considered, and that the empirical distributions of number of prey and number of predators follow universal functional forms that match the analytical predictions.
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