Graphs - or networks in colloquial language - consist of objects or nodes and relationships or edges. For neural networks, nodes would represent neurons, and edges their connections (i.e., synapses). In metabolic networks, nodes could be the substrates or molecules and edges could be reactions between molecules. From several studies during the last few years, one central - and very surprising - result emerged: real-life networks as different as brain connectivity, protein-protein interactions, food webs, social contacts, the Internet or highway transportation networks appear to share common architectonic principles.
All these networks are sparse, yet possess clusters, that is, regions with above-average connection density where neighbors of a node tend to be connected to each other. They also feature a highly efficient pathway structure, in which only a small number of intermediate connections have to be passed in order to get from one element to another. In relation to social networks this has long been known as the 'small-world' phenomenon.
The tools for analyzing networks arise from graph theory, statistical mechanics and social network analysis. Applying these methods was used for various real-world problems including the simulation of SARS virus spreading and possible immunization polocies, the identification of critical components in biochemical networks,or the relation between structure and function in the neural systems. As these methods can be applied to networks from different disciplines, this transdisciplinary field is often called Network Science.
Here you will find links to people, organizations and companies that are involved in network science. There is also information on books and courses if you want to find out more or want to get training in the emerging field of network science.