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Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries

Authors Raghavendra Addanki , Andrew McGregor, Cameron Musco

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Raghavendra Addanki
  • Adobe Research, San Jose, CA, USA
Andrew McGregor
  • Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA, USA
Cameron Musco
  • Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA, USA

Funding

This work was supported by a Dissertation Writing Fellowship awarded by the Manning College of Information and Computer Sciences, UMass Amherst to R. Addanki. In addition, this work was supported by NSF grants CCF-1934846, CCF-1908849, and CCF-1637536, awarded to A. McGregor; and NSF grants CCF-2046235, IIS-1763618, as well as Adobe and Google Research Grants, awarded to C. Musco.

Acknowledgements

Most of this work was done while R. Addanki was a student at UMass Amherst. Part of this work was done while R. Addanki was a visiting student at the Simons Institute for the Theory of Computing. We thank the anonymous reviewers for their helpful suggestions.

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Raghavendra Addanki, Andrew McGregor, and Cameron Musco. Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 2:1-2:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ESA.2022.2

Abstract

We study the problem of estimating the number of edges in an n-vertex graph, accessed via the Bipartite Independent Set query model introduced by Beame et al. (TALG '20). In this model, each query returns a Boolean, indicating the existence of at least one edge between two specified sets of nodes. We present a non-adaptive algorithm that returns a (1± ε) relative error approximation to the number of edges, with query complexity Õ(ε^{-5}log⁵ n), where Õ(⋅) hides poly(log log n) dependencies. This is the first non-adaptive algorithm in this setting achieving poly(1/ε,log n) query complexity. Prior work requires Ω(log² n) rounds of adaptivity. We avoid this by taking a fundamentally different approach, inspired by work on single-pass streaming algorithms. Moreover, for constant ε, our query complexity significantly improves on the best known adaptive algorithm due to Bhattacharya et al. (STACS '22), which requires O(ε^{-2} log^{11} n) queries. Building on our edge estimation result, we give the first {non-adaptive} algorithm for outputting a nearly uniformly sampled edge with query complexity Õ(ε^{-6} log⁶ n), improving on the works of Dell et al. (SODA '20) and Bhattacharya et al. (STACS '22), which require Ω(log³ n) rounds of adaptivity. Finally, as a consequence of our edge sampling algorithm, we obtain a Õ(n log^8 n) query algorithm for connectivity, using two rounds of adaptivity. This improves on a three-round algorithm of Assadi et al. (ESA '21) and is tight; there is no non-adaptive algorithm for connectivity making o(n²) queries.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • sublinear graph algorithms
  • bipartite independent set queries
  • edge sampling and counting
  • graph connectivity
  • query adaptivity

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