  Many real world networks, such as social networks, are primarily formed
through local interactions between agents. Additionally, in contrast with
common network models, social and biological networks exhibit a high degree of
clustering. Here we construct a class of network growth models based on local
interactions on a metric space, capable of producing arbitrary degree
distributions as well as a naturally high degree of clustering akin to
biological networks. As a specific example, we study the case of random-
walking agents, though most results hold for any linear stochastic dynamics.
Agents form bonds when they meet at designated locations we refer to as
"rendezvous points." The spatial distribution of the rendezvous points
determines key characteristics of the network such as the degree distribution.
For any arbitrary (monotonic) degree distribution, we are able to analytically
solve for the required rendezvous point distribution.
