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Exponential random graph models with soft clique constraints
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arXiv AI
Sep 1
Exponential random graph models with soft clique constraints
arXiv:2608.30869v1 Announce Type: cross Abstract: Let $r\geq3$ be fixed, and let $\mathbf{G}_n$ be the set of all simple graphs with vertex set $[n]=\{1,\ldots,n\}$. We consider an exponential random...
arXiv Machine Learning
Jun 15
Recovery thresholds for hidden weighted sparse graphs
arXiv:2606. 14335v1 Announce Type: cross Abstract: Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference.
arXiv Statistics ML
Aug 28
Explicit Bounds on the Entropy of Piecewise H\"{o}lder Graphon Models
The paper investigates the entropy of random graphs produced by piecewise Hölder continuous graphons. It establishes a convergence rate for the normalized entropy as graph size increases and outlines the main proof ideas, with full details in the appendix. Using this result, the authors derive explicit quantitative entropy bounds for both the stochastic block model and the random geometric graph model, moving beyond previous asymptotic statements.
arXiv AI
Jun 24
Random coloured digraphs defined by a Markov logic network
arXiv:2606. 23715v1 Announce Type: cross Abstract: A Markov Logic Network (MLN) is a probabilistic relational model used in Statistical Relational Artificial Intelligence for defining a probability distribution on the set of possible worlds with domain $D$ for an arbitrary finite domain $D$.
arXiv Machine Learning
Jun 18
Robust Detection of Planted Subgraphs in Semi-Random Models
arXiv:2508. 02158v2 Announce Type: replace-cross Abstract: Detection of planted subgraphs in Erd\"os-R\'enyi random graphs has been extensively studied, leading to a rich body of results characterizing both statistical and computational thresholds.
arXiv Machine Learning
Aug 18
Correlation Clustering with Random Partial Information
arXiv:2608. 16315v1 Announce Type: cross Abstract: Correlation clustering is a fundamental unsupervised learning problem.