Lower Bounds for Graph Reconstruction Using Maximal Independent Set Queries

Core Concepts

The minimum number of maximal independent set queries required to reconstruct the edges of a hidden graph with n vertices and maximum degree Δ is Ω(Δ^2 log(n/Δ)) for randomized non-adaptive algorithms and Ω(Δ^3 log n / log Δ) for deterministic non-adaptive algorithms.

Abstract

The paper investigates the minimum number of maximal independent set queries required to reconstruct the edges of a hidden graph. The key findings are: Randomized adaptive algorithms need at least Ω(Δ^2 log(n/Δ) / log Δ) queries to reconstruct n-vertex graphs of maximum degree Δ with success probability at least 1/2. Randomized non-adaptive algorithms need at least Ω(Δ^2 log(n/Δ)) queries to reconstruct n-vertex graphs of maximum degree Δ with success probability at least 1/2. This matches the upper bound of O(Δ^2 log n) for Δ ≤ n^(1-ε) where ε > 0 is fixed. Deterministic non-adaptive algorithms require at least Ω(Δ^3 log n / log Δ) queries to reconstruct n-vertex graphs of maximum degree Δ. This nearly matches the upper bound of O(Δ^3 log n) from prior work. The proofs relate the problem to cover-free families, for which the authors also provide improved lower bounds. The key idea is to consider graphs with a large clique and an independent set, where maximal independent set queries reveal little information.

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Lower bounds for graph reconstruction with maximal independent set queries

by Lukas Michel... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03472.pdf

Deeper Inquiries

What are the implications of these lower bounds for practical applications of graph reconstruction using maximal independent set queries

The lower bounds established for graph reconstruction using maximal independent set queries have significant implications for practical applications. These lower bounds provide insights into the minimum number of queries required to accurately reconstruct a hidden graph. In real-world scenarios where graph reconstruction is necessary, such as in network analysis, social network modeling, or biological network studies, understanding the lower bounds helps in optimizing the query process. By knowing the minimum number of queries needed, researchers and practitioners can design more efficient algorithms for graph reconstruction, reducing the computational resources and time required for the task. Additionally, these lower bounds offer a theoretical foundation for evaluating the performance of graph reconstruction algorithms and guiding the development of more effective strategies in various applications.

Can the gap between the upper and lower bounds for deterministic non-adaptive algorithms be further reduced

While the gap between the upper and lower bounds for deterministic non-adaptive algorithms has been reduced in this study, there is potential for further refinement. By exploring more intricate combinatorial structures and refining the analysis of cover-free families, it may be possible to narrow the gap even further. Researchers could investigate alternative approaches to establish tighter lower bounds, considering different graph properties and query mechanisms. Additionally, incorporating advanced mathematical techniques and exploring the connections between graph theory and information theory could lead to more precise lower bounds for deterministic non-adaptive algorithms in graph reconstruction.

Are there other query models beyond maximal independent set queries that could lead to more efficient graph reconstruction algorithms

Beyond maximal independent set queries, exploring other query models could indeed lead to more efficient graph reconstruction algorithms. For example, considering queries related to vertex covers, minimum spanning trees, or shortest paths could provide additional information about the graph structure and potentially reduce the number of queries required for reconstruction. By incorporating diverse query types and leveraging their unique properties, researchers can develop hybrid query strategies that offer a more comprehensive view of the hidden graph. Exploring the interplay between different query models and their impact on the reconstruction process could open up new avenues for optimizing graph reconstruction algorithms and enhancing their practical applicability.