Erasure-Resilient Sublinear-Time Graph Algorithms

Amit Levi, Ramesh Krishnan S. Pallavoor, Sofya Raskhodnikova, Nithin Varma

Research output: Contribution to journalArticlepeer-review

Abstract

We investigate sublinear-time algorithms that take partially erased graphs represented by adjacency lists as input. Our algorithms make degree and neighbor queries to the input graph and work with a specified fraction of adversarial erasures in adjacency entries. We focus on two computational tasks: testing if a graph is connected or ϵ-far from connected and estimating the average degree. For testing connectedness, we discover a threshold phenomenon: when the fraction of erasures is less than ϵ, this property can be tested efficiently (in time independent of the size of the graph); when the fraction of erasures is at least ϵ, then a number of queries linear in the size of the graph representation is required. Our erasure-resilient algorithm (for the special case with no erasures) is an improvement over the previously known algorithm for connectedness in the standard property testing model and has optimal dependence on the proximity parameter ϵ. For estimating the average degree, our results provide an "interpolation"between the query complexity for this computational task in the model with no erasures in two different settings: with only degree queries, investigated by Feige (SIAM J. Comput. 06), and with degree queries and neighbor queries, investigated by Goldreich and Ron (Random Struct. Algorithms 08) and Eden et al. (ICALP 17). We conclude with a discussion of our model and open questions raised by our work.

Original languageEnglish
Article number1
JournalACM Transactions on Computation Theory
Volume14
Issue number1
DOIs
StatePublished - Mar 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Association for Computing Machinery.

Keywords

  • Graph property testing
  • approximating graph parameters
  • computing with incomplete information

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics

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