Network Science (2005) / Chapter Skim
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Appendix C Content of Network Science Courses
Appendix C Content of Network Science Courses
Pages 60-64
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From page 60... ...
For example, The committee faced a significant challenge as it sought a survey of basic courses on, say, quantum mechanics or to synthesize the diverse body of material taught in a wide economics would show that while there is broad agreement range of disciplines. Indeed, as discussed in Chapter 2, net- across instructors, textbooks, and universities on a small set work science is called on to address problems that not only of topics that must be covered, there are significant variacut across disciplines but also represent a vast body of knowl- tions in the examples used, the application areas covered, edge, from infrastructure networks, such as the power grid and special topics.
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From page 61... ...
the nodes might be humans or scientists in social networks; Other important measures include the shortest path between molecules, genes, or neurons in biology; routers or trans- two nodes, which plays a key role in identifying small-world formers in infrastructural networks; and Web pages or re- effects; the diameter, which is the distance between the two search publications in information networks. Similarly, the most distant nodes; the subgraphs and communities that links might be friendships, alliances, reactions, synapses, characterize the relationship between small subsets of nodes optical and copper cables, URLs, or citations.
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From page 62... ...
First, Transportation networks some models aim to mimic, in a simplified form, the emer Supply chains and manufacturing gence and evolution of real networks, helping us to underResearch networks Scientific grid stand the mechanism responsible for the formation of real Collaborations networks. Second, to test the impact of selected network Blogs and online journals characteristics on the network's behavior, we need to generMilitary networks Terrorist networks Intelligence networks Logistics networks Biological networks Metabolism Gene and protein interactions Biomanufacturing TABLE C-3 Content of a Typical Network Science Regulatory and control networks Course Ecological networks and food webs Viruses and epidemics Subject Content Core concepts Real-world networks Characterization and classifying networks and their components Network modeling Network interpretation Flow and routing finally, the link strength or weight, which characterizes the and processes Aggregation and growth nature of the interactions between different nodes.
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From page 63... ...
ScaleRandom networks Exact methods free network models, which represent graphs that change in Erdos-Renyi model Discrete math time, are typically studied using methods based on rate and Percolation based Combinatorics Scale-free models Graph theory master equations capable of precisely predicting the degree Growth and preferential attachment Dynamical systems distribution and other characteristics of scale-free networks. Static models Master and rate equations In some cases mathematicians have employed exact meth Optimization Mean field theory ods and the tools of dynamical systems and percolation Static models Generating functions theory to obtain exact results for these networks.
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From page 64... ...
pears when it comes to network function, thanks to the di- In general, the network structure canalizes these flow proversity of the functions that networks assume in different cesses and to a high degree determines the flow rates and the domains. For example, the purpose of the Internet is to trans- necessary capacities on each link and node.
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