Papers Graph Generation
“Graph Generation” 태그가 달린 논문 831편 · 필터 해제
When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems
Multi-Agent Systems (MAS) have recently moved from static workflows toward dynamically generated collaboration topologies. However, existing topology generation methods rely primarily on the parametric knowledge of large…
Graph GenerationGromov-Monge Flow Matching for Equivariant Graph Generation
Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in generative models. In flow matching, however, symmetry may also enter the source--target coupling: once graph p…
Graph GenerationDiffusion Transformers for Roof Graph Synthesis and Reconstruction
We present RoofDiT, a generative framework for 2D roof graph synthesis and reconstruction. Roofs are compactly described as planar graphs of junctions and structural edges, but existing methods often rely on fixed geomet…
Graph GenerationReward-Guided Autoregressive Graph Generation for Efficient Multi-Agent Communication Topology Design
LLM-based Multi-Agent Systems (MAS) achieve strong performance on complex reasoning tasks by coordinating multiple agents, but at the cost of substantial token consumption. Recent work on automatic topology design, ARG-D…
Reinforcement LearningGraph GenerationGraphK: Variable-Size Graph Generation with Efficient Edge Construction
Graph generation models have advanced significantly with deep learning, yet they remain limited in scalability, flexibility, and ability to model underlying structures. We present GraphK, a novel encoder-sampler-decoder …
Computational EfficiencyGraph GenerationFRAGMENT: Factorized Graph Representations for Document Generation and Editing via Entity-Aware Transformations
Structured documents such as invoices, forms, reports, and scientific articles derive meaning from the interplay between spatial layout, textual content, and logical structure. Generative models operating at the pixel or…
Graph GenerationLLM-Guided Graph Generation for Structure-Based Local Improvement Methods
Large neighborhood search normally selects a random subset of decision variables for iterative optimization. To efficiently solve various problems, researchers tend to design variable selection strategies that take into …
Graph GenerationReversing Arrows in Large Language Models
Large language models (LLMs) have achieved strong performance on text-to-knowledge graph generation and related tasks. Nevertheless, it is still unclear whether they accurately model the direction-dependent semantics of …
Relation ClassificationGraph GenerationExpanding Flow Maps
Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet existing parameterizations are constrained to fixed dimensions or fixed s…
Graph GenerationSafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia
More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environments. This paper presents SafeStep, an AI-driven travel system that assists elderly…
Graph GenerationDiPhon: Diffusion on Graphons for Scalable Graph Generation
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem. We approach this question in…
Graph GenerationProximal Policy Optimization for Amortized Discrete Sampling
This paper explores policy gradient algorithms for training stochastic policies to sample from structured discrete probability distributions under the Generative Flow Network (GFlowNet) framework. Building on extensive t…
Reinforcement LearningGraph GenerationInference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models
Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally conditioned on their ancestors. This makes fa…
Graph GenerationQueryWeaver: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation
Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information. While LLMs show strong reasoning and tool use, reliably executin…
Natural Language QueriesGraph GenerationFLAGG: Flexible Autoregressive Graph Generation
The Deep Graph Generation's panorama spans two extremes: one-shot and sequential models. The former generates nodes and edges jointly, while the latter samples them autoregressively. Each method performs better in differ…
Graph GenerationScaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models
Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond. However, current graph generative models remain limited b…
Graph GenerationUncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather than deterministic values. Meanwhile, diffusion-based models for 3D…
Bayesian InferenceGraph GenerationAn Efficient and Scalable Graph Condensation with Structure-Preserving
Graph condensation (GC) is pivotal for enabling Graph Neural Networks (GNNs) deployment in resource-constrained scenarios by compressing large-scale graphs into compact synthetic counterparts. Existing GC methods commonl…
Computational EfficiencyGraph GenerationGraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce GraphARC, a benchmark for abstract reasoning on graph-structured data.…
Relational ReasoningNode ClassificationGraph GenerationLink PredictionEvolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach
Generating realistic graph-structured data is challenging due to discrete connectivity, varying graph sizes, and class-specific structural patterns. Recent Generative Adversarial Networks (GAN)-based graph generation met…
Data AugmentationGraph Generation