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Graph Reconstruction via MIS Queries

Authors Christian Konrad , Conor O'Sullivan, Victor Traistaru

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Christian Konrad
  • School of Computer Science, University of Bristol, UK
Conor O'Sullivan
  • School of Computer Science, University of Bristol, UK
Victor Traistaru
  • School of Computer Science, University of Bristol, UK

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Christian Konrad, Conor O'Sullivan, and Victor Traistaru. Graph Reconstruction via MIS Queries. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 66:1-66:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ITCS.2025.66

Abstract

In the Graph Reconstruction (GR) problem, a player initially only knows the vertex set V of an input graph G = (V, E) and is required to learn its set of edges E. To this end, the player submits queries to an oracle and must deduce E from the oracle’s answers. 
Angluin and Chen [Journal of Computer and System Sciences, 2008] resolved the number of Independent Set (IS) queries necessary and sufficient for GR on m-edge graphs. In this setting, each query consists of a subset of vertices U ⊆ V, and the oracle responds with a boolean, indicating whether U is an independent set in G. They gave algorithms that use O(m ⋅ log n) IS queries, which is best possible. 
In this paper, we initiate the study of GR via Maximal Independent Set (MIS) queries, a more powerful variant of IS queries. Given a query U ⊆ V, the oracle responds with any, potentially adversarially chosen, maximal independent set I ⊆ U in the induced subgraph G[U].
We show that, for GR, MIS queries are strictly more powerful than IS queries when parametrized by the maximum degree Δ of the input graph. We give tight (up to poly-logarithmic factors) upper and lower bounds for this problem:  
1) We observe that the simple strategy of taking uniform independent random samples of V and submitting those to the oracle yields a non-adaptive randomized algorithm that executes O(Δ² ⋅ log n) queries and succeeds with high probability. This should be contrasted with the fact that Ω(Δ ⋅ n ⋅ log(n/Δ)) IS queries are required for such graphs, which shows that MIS queries are strictly more powerful than IS queries. Interestingly, combining the strategy of taking uniform random samples of V with the probabilistic method, we show the existence of a deterministic non-adaptive algorithm that executes O(Δ³ ⋅ log(n/Δ)) queries.
2) Regarding lower bounds, we prove that the additional Δ factor when going from randomized non-adaptive algorithms to deterministic non-adaptive algorithms is necessary. We show that every non-adaptive deterministic algorithm requires Ω(Δ³ / log² Δ) queries. For arbitrary randomized adaptive algorithms, we show that Ω(Δ²) queries are necessary in graphs of maximum degree Δ, and that Ω(log n) queries are necessary, even when the input graph is an n-vertex cycle.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Query Complexity
  • Graph Reconstruction
  • Maximal Independent Set Queries

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