Global multi-level analysis of the 'scientific food web'.

We introduce a network-based index analyzing excess scientific production and consumption to perform a comprehensive global analysis of scholarly knowledge production and diffusion on the level of continents, countries, and cities. Compared to measures of scientific production and consumption such as number of publications or citation rates, our network-based citation analysis offers a more differentiated picture of the 'ecosystem of science'. Quantifying knowledge flows between 2000 and 2009, we identify global sources and sinks of knowledge production.

Bankruptcy cascades in interbank markets.

We study a credit network and, in particular, an interbank system with an agent-based model. To understand the relationship between business cycles and cascades of bankruptcies, we model a three-sector economy with goods, credit and interbank market. In the interbank market, the participating banks share the risk of bad debits, which may potentially spread a bank's liquidity problems through the network of banks.

Predicting scholars' scientific impact.

We tested the underlying assumption that citation counts are reliable predictors of future success, analyzing complete citation data on the careers of ~150,000 scientists. Our results show that i) among all citation indicators, the annual citations at the time of prediction is the best predictor of future citations, ii) future citations of a scientist's published papers can be predicted accurately (r(2) = 0.80 for a 1-year prediction, P<0.

How citation boosts promote scientific paradigm shifts and nobel prizes.

Nobel Prizes are commonly seen to be among the most prestigious achievements of our times. Based on mining several million citations, we quantitatively analyze the processes driving paradigm shifts in science. We find that groundbreaking discoveries of Nobel Prize Laureates and other famous scientists are not only acknowledged by many citations of their landmark papers.

The spatial variability of vehicle densities as determinant of urban network capacity.

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far have been based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model.