An interesting talk from the Santa Fe Institute on how understanding large-scale networks (the speaker, Mark Newman, works primarily with social networks) can help us understand complex systems.
What the large scale structure of networks can tell us about many kinds of complex systems
June 5, 2014 | Santa Fe Institute
Networks are useful as compact mathematical representations of all sorts of systems. SFI External Professor Mark Newman asks what the large-scale mathematical structures of networks can tell us.
Mathematical measures of network properties such as degree (a measure of average connectivity) and transitivity (a measure of second-order connectivity) are simple, often-used ways of understanding network structure at a local level.
Newman is interested in larger-scale structures of networks with thousands or millions of nodes. He reviews statistical techniques that offer such large-scale insights, as well as potential predictive capabilities.
His presentation took place during SFI's 2014 Science Board Symposium in Santa Fe.
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