I have the following random graph model, and I am looking for its name and/or any work done concerning it.
Given $n$ nodes, $\{v_1,...,v_n\}$, and $n$ timesteps, proceed as follows: On the $k$th timestep, choose a node, $v_j$ uniformly at random from $\{v_1,...,v_{k-1}, v_{k+1},...,v_n\}$ and add the undirected edge $(v_k, v_j)$ to the graph. Note that this edge may have been added at an earlier timestep, in which case skip adding an edge and proceed to the next timestep.
As an example, for $n=3$:
1) Add edge $(v_1, v_2)$ to the graph.
2) Add edge $(v_2, v_1)$ to the graph. This edge already exists, so do nothing instead.
3) Add edge $(v_3, v_1)$ to the graph.
4) The final graph has two edges, one between $v_1$ and $v_2$ and one between $v_1$ and $v_3$
This generative model has some interesting properties. It has a fixed average degree with each node having degree at least one. It is also my intuition that it minimizes or comes very close to minimizing clustering for a fixed number of edges. I'm not familiar with any models which behave as the one you are describing. Good for you, it's hard to come up with new models nowadays =).