The k-core decomposition is a fundamental primitive in many machine learning and data mining applications. We present the first distributed and the first streaming algorithms to compute and maintain an approximate k-core decomposition with provable guarantees. Our algorithms achieve rigorous bounds on space complexity while bounding the number of passes or number of rounds of computation. We do so by presenting a new powerful sketching technique for k-core decomposition, and then by showing it can be computed efficiently in both streaming and MapReduce models. Finally, we confirm the effectiveness of our sketching technique empirically on a number of publicly available graphs.
@article{arxiv.1808.02546,
title = {Parallel and Streaming Algorithms for K-Core Decomposition},
author = {Hossein Esfandiari and Silvio Lattanzi and Vahab Mirrokni},
journal= {arXiv preprint arXiv:1808.02546},
year = {2018}
}