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.
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.