An optimization approach to locallybiased graph algorithms
Abstract
Locallybiased graph algorithms are algorithms that attempt to find local or smallscale 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 locallybiased 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 does not correspond well with the way that algorithmic and statistical theory is typically formulated. Recent work from several research communities has focused on developing locallybiased 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 seeminglydifferent approaches, and highlighting promising directions for future work.
 Publication:

arXiv eprints
 Pub Date:
 July 2016
 arXiv:
 arXiv:1607.04940
 Bibcode:
 2016arXiv160704940F
 Keywords:

 Computer Science  Social and Information Networks;
 Computer Science  Data Structures and Algorithms
 EPrint:
 19 pages, 13 figures