R-MAT: A Recursive Model for Graph Mining

How does a `normal' computer (or social) network look like? How can we spot `abnormal' sub-networks in the Internet, or web graph? The answer to such questions is vital for outlier detection (terrorist networks, or illegal money-laundering rings), forecasting, and simulations (\how will a computer virus spread?"). The heart of the problem is finding the properties of real graphs that seem to persist over multiple disciplines. We list such "laws" and, more importantly, we propose a simple, parsimonious model, the "recursive matrix" (R-MAT) model, which can quickly generate realistic graphs, capturing the essence of each graph in only a few parameters. Contrary to existing generators, our model can trivially generate weighted, directed and bipartite graphs; it subsumes the celebrated Erdos-Renyi model as a special case; it can match the power law behaviors, as well as the deviations from them (like the \winner does not take it all" model of Pennock et al. [20]). We present results on multiple, large real graphs, where we show that our parameter fitting algorithm (AutoMAT-fast) fits them very well.