Sublinear Algorithms for MAXCUT and Correlation Clustering

Authors Aditya Bhaskara, Samira Daruki, Suresh Venkatasubramanian

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Author Details

Aditya Bhaskara
  • School of Computing, University of Utah, USA,
Samira Daruki
  • Expedia Research, USA,
Suresh Venkatasubramanian
  • School of Computing, University of Utah, USA,

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Aditya Bhaskara, Samira Daruki, and Suresh Venkatasubramanian. Sublinear Algorithms for MAXCUT and Correlation Clustering. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 16:1-16:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


We study sublinear algorithms for two fundamental graph problems, MAXCUT and correlation clustering. Our focus is on constructing core-sets as well as developing streaming algorithms for these problems. Constant space algorithms are known for dense graphs for these problems, while Omega(n) lower bounds exist (in the streaming setting) for sparse graphs. Our goal in this paper is to bridge the gap between these extremes. Our first result is to construct core-sets of size O~(n^{1-delta}) for both the problems, on graphs with average degree n^{delta} (for any delta >0). This turns out to be optimal, under the exponential time hypothesis (ETH). Our core-set analysis is based on studying random-induced sub-problems of optimization problems. To the best of our knowledge, all the known results in our parameter range rely crucially on near-regularity assumptions. We avoid these by using a biased sampling approach, which we analyze using recent results on concentration of quadratic functions. We then show that our construction yields a 2-pass streaming (1+epsilon)-approximation for both problems; the algorithm uses O~(n^{1-delta}) space, for graphs of average degree n^delta.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sketching and sampling
  • Sublinear algorithms
  • Streaming algorithms
  • Core-sets
  • Maximum cut
  • Correlation clustering


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