arXiv:2511.17240v3 Announce Type: replace-cross Abstract: We study the problem of learning an unknown graph via group queries on node subsets, where each query reports whether at least one edge is present among the queried nodes. In general, learning arbitrary graphs with $n$ nodes and $k$ edges is hard in the non-adaptive setting, requiring $\Omega\big(\min\{k^2\log n,\,n^2\}\big)$ tests even when a small error probability is allowed. We focus on learning Erd\H{o}s--R\'enyi (ER) graphs $G\sim\mathrm{ER}(n,q)$ in the non-adaptive setting, where the expected number of edges is $\bar{k}=q\binom{
This academic paper details a theoretical advance in a specific graph learning problem, building on prior work in the field of non-adaptive learning.
While a theoretical contribution, advancements in graph learning algorithms can eventually contribute to more efficient AI systems and data analysis.
This paper presents a more efficient computational method for a particular type of graph learning, but it does not represent an immediate practical change.
Improved theoretical understanding of learning Erdős–Rényi graphs under specific constraints.
Potential for this method or its principles to be incorporated into broader graph-based machine learning approaches in the distant future.
Very long-term and indirect contribution to the generalized efficiency of certain AI algorithms.
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