Papers Graph Generation
“Graph Generation” 태그가 달린 논문 831편 · 필터 해제
BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation
Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify semantic equivalences between input data sources and ontology terms. Wh…
Graph GenerationKnowledge GraphsPlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation
Accurate estimation of plant skeletal structures (e.g., branching structures) from images is essential for smart agriculture and plant science. Unlike human skeletons with fixed topology, plant skeleton estimation presen…
Graph GenerationControllable Molecular Generative Foundation Models
Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability. While reinforcement learning (RL) …
Reinforcement LearningGraph GenerationDrug DiscoveryFine-Grained Graph Generation through Latent Mixture Scheduling
Structure aware graph generation aims to generate graphs that satisfy given topological properties. It has applications in domains such as drug discovery, social network modeling, and knowledge graph construction. Unlike…
Graph GenerationDrug DiscoveryInformation-geometric adaptive sampling for graph diffusion
Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of distributional evolution on complex manifolds. In this paper, we present …
Graph GenerationLearning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each ne…
Node ClassificationGraph GenerationGraph LearningOne Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
Knowledge Graphs (KGs) are composed of triples, and the goal of Knowledge Graph Completion (KGC) is to infer the missing factual triples. Traditional KGC tasks predict missing elements in a triple given one or two of its…
Knowledge Graph CompletionGraph GenerationKnowledge GraphsInterpolating Discrete Diffusion Models with Controllable Resampling
Discrete diffusion models form a powerful class of generative models across diverse domains, including text and graphs. However, existing approaches face fundamental limitations. Masked diffusion models suffer from irrev…
Graph GenerationText GenerationPrism: Symbolic Superoptimization of Tensor Programs
This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large classes of tensor programs by symbolically re…
Graph GenerationEquivariant Efficient Joint Discrete and Continuous MeanFlow for Molecular Graph Generation
Graph-structured data jointly contain discrete topology and continuous geometry, which poses fundamental challenges for generative modeling due to heterogeneous distributions, incompatible noise dynamics, and the need fo…
Graph GenerationBeyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation
Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas sche…
Graph GenerationKnowledge GraphsGuideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs
Clinical practice guidelines are long, multimodal documents whose branching recommendations are difficult to convert into executable clinical decision support (CDS), and one-shot parsing often breaks cross-page continuit…
Graph GenerationHierarchical Discrete Flow Matching for Graph Generation
Denoising-based models, including diffusion and flow matching, have led to substantial advances in graph generation. Despite this progress, such models remain constrained by two fundamental limitations: a computational c…
Graph GenerationUnrestrained Simplex Denoising for Discrete Data. A Non-Markovian Approach Applied to Graph Generation
Denoising models such as Diffusion or Flow Matching have recently advanced generative modeling for discrete structures, yet most approaches either operate directly in the discrete state space, causing abrupt state change…
Graph GenerationHierarchy-Guided Topology Latent Flow for Molecular Graph Generation
Generating chemically valid 3D molecules is hindered by discrete bond topology: small local bond errors can cause global failures (valence violations, disconnections, implausible rings), especially for drug-like molecule…
Graph GenerationReservoir-Based Graph Convolutional Networks
Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convolutional Networks (GCNs) exemplify this …
Graph ClassificationGraph GenerationHGNet: Scalable Foundation Model for Automated Knowledge Graph Generation from Scientific Literature
Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi-word entities, often fail to gene…
Relation ExtractionGraph GenerationGraph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, probabilistic inference is particularly v…
Graph GenerationFlowMS: Flow Matching for De Novo Structure Elucidation from Mass Spectra
Mass spectrometry (MS) stands as a cornerstone analytical technique for molecular identification, yet de novo structure elucidation from spectra remains challenging due to the combinatorial complexity of chemical space a…
Graph GenerationGenerative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy
We review thirteen generative systems and five supporting datasets for quantum circuit and quantum code generation, identified through a structured scoping review of Hugging Face, arXiv, and provenance tracing (January-F…
Graph GenerationCode Generation