Network Entropy — concentration of exposure

Two webs with identical amplification distance can fail differently: one through a handful of hubs, one everywhere at once. This lens measures that difference as entropy over the exposure distribution — with exp(H) as the honest 'how many nodes effectively matter' number.

How it reads

h_norm in [0,1]: low = concentrated (few-hub), high = dispersed. effective_n = exp(H) reads as 'the web behaves like N equal nodes'. hhi is the familiar concentration cross-check.