  The social percolation model \citep{solomon-et-00} considers a 2-dimensional
regular lattice. Each site is occupied by an agent with a preference $x_{i}$
sampled from a uniform distribution $U[0,1]$. Agents transfer the information
about the quality $q$ of a movie to their neighbors only if $x_{i}\leq q$.
Information percolates through the lattice if $q=q_{c}=0.593$. -- From a
network perspective the percolating cluster can be seen as a random-regular
network with $n_{c}$ nodes and a mean degree that depends on $q_{c}$.
Preserving these quantities of the random-regular network, a true random
network can be generated from the $G(n,p)$ model after determining the link
probability $p$. I then demonstrate how this random network can be transformed
into a threshold network, where agents create links dependent on their $x_{i}$
values. Assuming a dynamics of the $x_{i}$ and a mechanism of group formation,
I further extend the model toward an adaptive social network model.
