The Emergence of Informative Higher Scales in Complex Networks
Effective information depends on network structure. (a) In Erdős-Rényi (ER) networks, we see the network’s level off at as , the network’s size, increases (log scale shown). (b) The of networks grown under a preferential attachment mechanism, which depends on the preferential attachment exponent, . Under this network growth model, new nodes add their edges (here, ) to existing nodes in the network with a probability proportional to . Only sublinear preferential attachment allows for the continuous growth of with the growth of the network. The ribbons around the data represent standard deviations after 100 simulations of each.
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