  Network data is ubiquitous and growing, yet we lack realistic generative
network models that can be calibrated to match real-world data. The recently
proposed Block Two-Level Erdss-Renyi (BTER) model can be tuned to capture two
fundamental properties: degree distribution and clustering coefficients. The
latter is particularly important for reproducing graphs with community
structure, such as social networks. In this paper, we compare BTER to other
scalable models and show that it gives a better fit to real data. We provide a
scalable implementation that requires only O(d_max) storage where d_max is the
maximum number of neighbors for a single node. The generator is trivially
parallelizable, and we show results for a Hadoop MapReduce implementation for a
modeling a real-world web graph with over 4.6 billion edges. We propose that
the BTER model can be used as a graph generator for benchmarking purposes and
provide idealized degree distributions and clustering coefficient profiles that
can be tuned for user specifications.
