Every fixed first-order sentence $\varphi$ determines an enumerative sequence $n\mapsto\mathrm{FOMC}(\varphi,n)$, counting its models on the labeled domain $[n]$. We study the complexity of these sequences when logical specifications may use genuine unary function symbols and hence nested terms $x,f(x),f^2(x),\ldots$. We first prove that, for every fixed sentence $\varphi\in\mathrm{C}^1_{=}[f]$, with one unary function and an arbitrary finite relational vocabulary, $\mathrm{FOMC}(\varphi,n)$ is computable in time polynomial in $n$. By contrast, permitting either a second variable or a second unary function already yields hardness. Without counting quantifiers, there is a fixed sentence in $\mathrm{FO}^2_{=}[f]$ whose model-counting function is $\#\mathrm{P}_1$-complete. With one variable and two unary functions, there is a fixed constant-free universal sentence in $\mathrm{FO}^1_{=}[f,g]$, using only unary predicates besides $f$ and $g$, whose model-counting function is again $\#\mathrm{P}_1$-complete. We also relate labeled and unlabeled enumeration exactly. For every relational sentence $\varphi$, we construct an extension $\varphi_{\mathrm{aut}}$ in which a unary function records an automorphism and $\mathrm{FOMC}(\varphi_{\mathrm{aut}},n)=n!\cdot\mathrm{UFOMC}(\varphi,n)$, where $\mathrm{UFOMC}(\varphi,n)$ denotes the number of $n$-element models of $\varphi$ up to isomorphism. Thus automorphism marking gives a one-query exact reduction from unlabeled to labeled model counting at the same domain size. Over relational vocabularies of maximum arity at most $k$, where $k\geq2$, eliminating the auxiliary function yields single-query reductions from unlabeled $\mathrm{FO}^k_{=}$ and $\mathrm{C}^k$ model counting to labeled $\mathrm{FO}^{k+1}_{=}$ and $\mathrm{C}^{k+1}$ model counting, respectively.
Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for small relational structures, they underperform diffusion-based generative graph models on larger structures. In this paper, we strengthen NMLNs along two main dimensions: (i) we increase the expressive capacity of their potential functions using graph neural networks, and (ii) we develop a new training and inference algorithm inspired by parallel-tempering Markov chain Monte Carlo methods, which we name parallel noising. Together, these enhancements enable NMLNs to attain strong performance in graph generation relative to general diffusion-based generative graph models. Furthermore, they allow NMLNs to match the performance of specialized text-based recurrent models when generating small molecular structures.
Peter Jung, Giuseppe Marra, Ondřej Kuželka· 0 citations