arXiv AI
Flowette: Flow Matching with Graphette Priors for Graph Generation
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arXiv Machine Learning
Jun 19
Prior-Informed Flow Matching for Graph Reconstruction
arXiv:2601. 22107v2 Announce Type: replace Abstract: We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction.
arXiv Machine Learning
Sep 24
Variational Bayesian Flow Network for Graph Generation
The paper introduces Variational Bayesian Flow Network (VBFN), a graph generation model that lifts Bayesian updates to a joint Gaussian belief family with structured precisions, enabling coupled node and edge updates in a single fusion step. By constructing sample‑agnostic sparse precisions from a representation‑induced dependency graph, VBFN avoids label leakage while enforcing node‑edge consistency. Experiments on synthetic and molecular graph datasets show that VBFN improves fidelity and diversity over baseline methods.
arXiv Machine Learning
Sep 30
Alignment Matters Inside and Out in Equivariant Graph Flow Matching
The paper investigates how alignment—both outer (choosing which graphs to pair) and inner (aligning node representatives)—affects permutation-equivariant graph flow matching. It connects inner alignment to transport on the graph quotient space and shows that quotient couplings can be lifted to aligned representatives, while symmetrization yields equivariant flow‑matching minimizers. Experiments on continuous graph and molecular generation demonstrate that appropriate alignment can simplify trajectories and improve few‑step generation, though the benefits vary with the type of alignment and computational budget.
arXiv AI
Jun 11
From Uniform to Learned Graph Priors: Diffusion for Structure Discovery
arXiv:2606. 11831v1 Announce Type: cross Abstract: Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges.
Hugging Face Trending Papers
Oct 24
Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks
Graph Neural Networks are widely used to learn structural representations of relational data. Hierarchical graph pooling learns multi-level representations by progressively coarsening graphs; however,...
arXiv Machine Learning
Oct 2
RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis
RetroGEF is a flow-based generative model designed for single-step retrosynthesis, which constructs possible reactants by adding atoms and altering bonds directly from a target molecule’s graph. Unlike traditional methods that rely on a fixed-size canvas or a prescribed edit order, RetroGEF models both connectivity changes and graph size variations within a single generative process, learning directly from product–reactant pairs. Experiments on standard retrosynthesis benchmarks show that RetroGEF achieves state‑of‑the‑art performance.