Graph Reconstruction with a Connected Components Oracle
September 05, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Juha Harviainen, Pekka Parviainen
arXiv ID
2509.05002
Category
cs.DS: Data Structures & Algorithms
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In the Graph Reconstruction (GR) problem, the goal is to recover a hidden graph by utilizing some oracle that provides limited access to the structure of the graph. The interest is in characterizing how strong different oracles are when the complexity of an algorithm is measured in the number of performed queries. We study a novel oracle that returns the set of connected components (CC) on the subgraph induced by the queried subset of vertices. Our main contributions are as follows: 1. For a hidden graph with $n$ vertices, $m$ edges, maximum degree $Ξ$, and treewidth $k$, GR can be solved in $O(\min\{m / \log m, Ξ^2, k^2\} \cdot \log n)$ CC queries by an adaptive randomized algorithm. 2. For a hidden graph with $n$ vertices and degeneracy $d$, GR can be solved in $O(d^2 \log^2 n)$ CC queries by an adaptive randomized algorithm. 3. For a hidden graph with $n$ vertices, $m$ edges, maximum degree $Ξ$, and treewidth $k$, no algorithm can solve GR in $o(\min\{m, Ξ^2, k^2\})$ CC queries.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted