An optimization approach to locally-biased graph algorithms [article]

Kimon Fountoulakis, David Gleich, Michael Mahoney
2016 arXiv   pre-print
Locally-biased graph algorithms are algorithms that attempt to find local or small-scale structure in a large data graph. In some cases, this can be accomplished by adding some sort of locality constraint and calling a traditional graph algorithm; but more interesting are locally-biased graph algorithms that compute answers by running a procedure that does not even look at most of the input graph. This corresponds more closely to what practitioners from various data science domains do, but it
more » ... es not correspond well with the way that algorithmic and statistical theory is typically formulated. Recent work from several research communities has focused on developing locally-biased graph algorithms that come with strong complementary algorithmic and statistical theory and that are useful in practice in downstream data science applications. We provide a review and overview of this work, highlighting commonalities between seemingly-different approaches, and highlighting promising directions for future work.
arXiv:1607.04940v3 fatcat:7jifluhe3zgjdcfyfpiq4tt6ya