Parallel and Streaming Algorithms for K-Core Decomposition

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Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1396-1405, 2018.

Abstract

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.

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