Papers Graph Generation
“Graph Generation” 태그가 달린 논문 831편 · 필터 해제
3D Molecule Generation from Rigid Motifs via SE(3) Flows
Three-dimensional molecular structure generation is typically performed at the level of individual atoms, yet molecular graph generation techniques often consider fragments as their structural units. Building on the adva…
Graph GenerationBuilding Deep Graph Predictors with Graph Imitation Learning
Recent years have seen substantial progress in neural generation of text, images, and audio, supported by mature training pipelines and large-scale optimization. For graphs, however, comparable progress has been more lim…
Graph GenerationPrincipled Latent Diffusion for Graphs via Laplacian Autoencoders
Graph diffusion models achieve state-of-the-art performance in graph generation but suffer from quadratic complexity in the number of nodes -- and much of their capacity is wasted modeling the absence of edges in sparse …
Graph GenerationLarge Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it oft…
Multi-agent Reinforcement LearningGraph GenerationReliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs
Retrieval-Augmented Generation for software engineering often relies on vector similarity search, which captures topical similarity but can fail on multi-hop architectural reasoning such as controller to service to repos…
Graph GenerationKnowledge GraphsGraph Inference Towards ICD Coding
Automated ICD coding involves assigning standardized diagnostic codes to clinical narratives. The vast label space and extreme class imbalance continue to challenge precise prediction. To address these issues, LabGraph i…
Reinforcement LearningDomain AdaptationGraph Generation†DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems
Chain-of-Thought (CoT) prompting is widely adopted for mathematical problem solving, including in low-resource languages, yet its behavior under irrelevant context remains underexplored. To systematically study this chal…
Mathematical ReasoningGraph GenerationGEnSHIN: Graphical Enhanced Spatio-temporal Hierarchical Inference Network for Traffic Flow Prediction
With the acceleration of urbanization, intelligent transportation systems have an increasing demand for accurate traffic flow prediction. This paper proposes a novel Graph Enhanced Spatio-temporal Hierarchical Inference …
Graph GenerationLinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis
Designing mechanical linkages to achieve target end-effector trajectories presents a fundamental challenge due to the intricate coupling between continuous node placements, discrete topological configurations, and nonlin…
Graph GenerationEvaluating Small Language Models for Agentic On-Farm Decision Support Systems
Large Language Models (LLM) hold potential to support dairy scholars and farmers by supporting decision-making and broadening access to knowledge for stakeholders with limited technical expertise. However, the substantia…
Computational EfficiencyGraph GenerationBeyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-world networks for various applications. G…
Graph ClassificationGraph GenerationTopology-Guided Quantum GANs for Constrained Graph Generation
Quantum computing (QC) promises theoretical advantages, benefiting computational problems that would not be efficiently classically simulatable. However, much of this theoretical speedup depends on the quantum circuit de…
Graph GenerationFrom Graphs to Gates: DNS-HyXNet, A Lightweight and Deployable Sequential Model for Real-Time DNS Tunnel Detection
Domain Name System (DNS) tunneling remains a covert channel for data exfiltration and command-and-control communication. Although graph-based methods such as GraphTunnel achieve strong accuracy, they introduce significan…
Multi-class ClassificationGraph GenerationA graph generation pipeline for critical infrastructures based on heuristics, images and depth data
Virtual representations of physical critical infrastructures, such as water or energy plants, are used for simulations and digital twins to ensure resilience and continuity of their services. These models usually require…
Instance SegmentationGraph GenerationObject DetectionPoint CloudsGraph VQ-Transformer (GVT): Fast and Accurate Molecular Generation via High-Fidelity Discrete Latents
The de novo generation of molecules with desirable properties is a critical challenge, where diffusion models are computationally intensive and autoregressive models struggle with error propagation. In this work, we intr…
Graph GenerationInvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation
Large Language Models (LLMs) have revolutionized the ability to understand and generate text, enabling significant progress in automatic knowledge graph construction from text (Text2KG). Many Text2KG methods, however, re…
Graph GenerationLGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
Graph generation is a critical task across scientific domains. Existing methods fall broadly into two categories: autoregressive models, which iteratively expand graphs, and one-shot models, such as diffusion, which gene…
Graph GenerationOntoMetric: An Ontology-Driven LLM-Assisted Framework for Automated ESG Metric Knowledge Graph Generation
Environmental, Social, and Governance (ESG) metric knowledge is inherently structured, connecting industries, reporting frameworks, metric categories, metrics, and calculation models through compositional dependencies, y…
Graph GenerationKnowledge GraphsWeaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting
Spatiotemporal forecasting on transportation networks is a complex task that requires understanding how traffic nodes interact within a dynamic, evolving system dictated by traffic flow dynamics and social behavioral pat…
Graph GenerationProcGen3D: Learning Neural Procedural Graph Representations for Image-to-3D Reconstruction
We introduce ProcGen3D, a new approach for 3D content creation by generating procedural graph abstractions of 3D objects, which can then be decoded into rich, complex 3D assets. Inspired by the prevalent use of procedura…
3D ReconstructionGraph Generation