2 research outputs found
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs
We describe the first sub-quadratic sampling algorithm for the Multiplicative
Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close
connection between MAGM and the Kronecker Product Graph Model (KPGM) of
Leskovec et al. (2010), and show that to sample a graph from a MAGM it suffices
to sample small number of KPGM graphs and \emph{quilt} them together. Under a
restricted set of technical conditions our algorithm runs in time, where is the number of nodes and is the number of edges
in the sampled graph. We demonstrate the scalability of our algorithm via
extensive empirical evaluation; we can sample a MAGM graph with 8 million nodes
and 20 billion edges in under 6 hours