Optimal Graph Reconstruction by Counting Connected Components in Induced Subgraphs

June 10, 2025 Β· Declared Dead Β· πŸ› Annual Conference Computational Learning Theory

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Authors Hadley Black, Arya Mazumdar, Barna Saha, Yinzhan Xu arXiv ID 2506.08405 Category cs.DS: Data Structures & Algorithms Cross-listed cs.IT, cs.LG Citations 2 Venue Annual Conference Computational Learning Theory Last Checked 4 months ago
Abstract
The graph reconstruction problem has been extensively studied under various query models. In this paper, we propose a new query model regarding the number of connected components, which is one of the most basic and fundamental graph parameters. Formally, we consider the problem of reconstructing an $n$-node $m$-edge graph with oracle queries of the following form: provided with a subset of vertices, the oracle returns the number of connected components in the induced subgraph. We show $Θ(\frac{m \log n}{\log m})$ queries in expectation are both sufficient and necessary to adaptively reconstruct the graph. In contrast, we show that $Ω(n^2)$ non-adaptive queries are required, even when $m = O(n)$. We also provide an $O(m\log n + n\log^2 n)$ query algorithm using only two rounds of adaptivity.
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