Matrices have broad ramifications in computer science ScienceBlog.com (blog) Of course, reducing the complex dynamics of weather-system models to a system of linear equations is itself a difficult task.
... naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of the hierarchical nature of our world.
Why Cognition-as-a-Service is the next operating system battlefield GigaOM Stephen Wolfram announced the Wolfram Language, which models the world and combines both programs and data — what he calls a new “language for the global brain,” — that will...
Network robustness research aims at finding a measure to quantify network robustness. Once such a measure has been established, we will be able to compare networks, to improve existing networks and to design new networks that are able to continue to perform well when it is subject to failures or attacks. In this paper we survey a large amount of robustness measures on simple, undirected and unweighted graphs, in order to offer a tool for network administrators to evaluate and improve the robustness of their network. The measures discussed in this paper are based on the concepts of connectivity (including reliability polynomials), distance, betweenness and clustering. Some other measures are notions from spectral graph theory, more precisely, they are functions of the Laplacian eigenvalues. In addition to surveying these graph measures, the paper also contains a discussion of their functionality as a measure for topological network robustness.
Graph measures and network robustness W. Ellens, R.E. Kooij
Using genetic algorithms to discover new nanostructured materials Phys.Org "In a sense, we are leveraging how nature discovers new materials—the Darwinian model of evolution—by suitably marrying it with computational methods.
Gershenson, C. & M. A. Niazi (2013). Multidisciplinary applications of complex networks modeling, simulation, visualization, and analysis. Complex Adaptive Systems Modeling 1:17 http://dx.doi.org/10.1186/2194-3206-1-17.