R-MAT: A recursive model for graph mining

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R-MAT: A recursive model for graph mining (2004)

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by Deepayan Chakrabarti , Yiping Zhan , Christos Faloutsos

Venue:	In Fourth SIAM International Conference on Data Mining (SDM’ 04
Citations:	245 - 17 self

BibTeX

@INPROCEEDINGS{Chakrabarti04r-mat:a,
    author = {Deepayan Chakrabarti and Yiping Zhan and Christos Faloutsos},
    title = {R-MAT: A recursive model for graph mining},
    booktitle = {In Fourth SIAM International Conference on Data Mining (SDM’ 04},
    year = {2004}
}

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Abstract

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 Erdős-Rényi 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. [21]). We present results on multiple, large real graphs, where we show that our parameter fitting algorithm (AutoMAT-fast) fits them very well. 1

Keyphrases

graph mining recursive model parsimonious model bipartite graph real graph special case power law behavior recursive matrix computer virus spread illegal money-laundering ring large real graph normal computer network look terrorist network realistic graph abnormal sub-networks multiple discipline present result celebrated erd s-r nyi model

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