GraphWeave learns graph family patterns via random walk trajectories and reconstructs new graphs through joint optimization, outperforming diffusion baselines on benchmarks for structures like communities and degree distributions while running 10x faster.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows. We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties.
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EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
BadGraph poisons training data with textual triggers to implant backdoors in latent diffusion models for text-guided graph generation, achieving 50% attack success rate at under 10% poisoning and over 80% at 24% poisoning with negligible clean performance loss.
EQUIMF is a unified equivariant framework that jointly generates discrete topologies and continuous geometries in molecular graphs via synchronized MeanFlow dynamics for efficient few-step sampling.
citing papers explorer
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GraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories
GraphWeave learns graph family patterns via random walk trajectories and reconstructs new graphs through joint optimization, outperforming diffusion baselines on benchmarks for structures like communities and degree distributions while running 10x faster.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
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BadGraph: A Backdoor Attack Against Latent Diffusion Model for Text-Guided Graph Generation
BadGraph poisons training data with textual triggers to implant backdoors in latent diffusion models for text-guided graph generation, achieving 50% attack success rate at under 10% poisoning and over 80% at 24% poisoning with negligible clean performance loss.
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Equivariant Efficient Joint Discrete and Continuous MeanFlow for Molecular Graph Generation
EQUIMF is a unified equivariant framework that jointly generates discrete topologies and continuous geometries in molecular graphs via synchronized MeanFlow dynamics for efficient few-step sampling.