  If each node of an idealized network has an equal capacity to efficiently
exchange benefits, then the network's capacity to use energy is scaled by the
average amount of energy required to connect any two of its nodes. The scaling
factor equals \textit{e}, and the network's entropy is $\ln(n)$. Networking
emerges in consequence of nodes minimizing the ratio of their energy use to the
benefits obtained for such use, and their connectability. Networking leads to
nested hierarchical clustering, which multiplies a network's capacity to use
its energy to benefit its nodes. Network entropy multiplies a node's capacity.
For a real network in which the nodes have the capacity to exchange benefits,
network entropy may be estimated as $C \log_L(n)$, where the base of the log is
the path length $L$, and $C$ is the clustering coefficient. Since $n$, $L$ and
$C$ can be calculated for real networks, network entropy for real networks can
be calculated and can reveal aspects of emergence and also of economic,
biological, conceptual and other networks, such as the relationship between
rates of lexical growth and divergence, and the economic benefit of adding
customers to a commercial communications network. \textit{Entropy dating} can
help estimate the age of network processes, such as the growth of hierarchical
society and of language.
