Papers Graph Generation
“Graph Generation” 태그가 달린 논문 831편 · 필터 해제
SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average error minimization approaches underperform in these highly relevant cases, a…
Graph GenerationEvolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability under distribution shifts, which is inevit…
Graph GenerationGraph LearningDiscrete Bayesian Sample Inference for Graph Generation
Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their discrete, unordered nature makes them difficult for traditional generative …
Graph GenerationKnowledge GraphsA Novel Framework for Multi-Modal Protein Representation Learning
Accurate protein function prediction requires integrating heterogeneous intrinsic signals (e.g., sequence and structure) with noisy extrinsic contexts (e.g., protein-protein interactions and GO term annotations). However…
Protein Function PredictionRepresentation LearningGraph GenerationLightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data
The scarcity of high-quality knowledge graphs (KGs) remains a critical bottleneck for downstream AI applications, as existing extraction methods rely heavily on error-prone pattern-matching techniques or resource-intensi…
Graph GenerationKnowledge GraphsBadGraph: A Backdoor Attack Against Latent Diffusion Model for Text-Guided Graph Generation
The rapid progress of graph generation has raised new security concerns, particularly regarding backdoor vulnerabilities. Though prior work has explored backdoor attacks against diffusion models for image or unconditiona…
Graph GenerationDrug DiscoveryAutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowled…
Reinforcement LearningQuestion AnsweringGraph GenerationKnowledge GraphsControllable Graph Generation with Diffusion Models via Inference-Time Tree Search Guidance
Graph generation is a fundamental problem in graph learning with broad applications across Web-scale systems, knowledge graphs, and scientific domains such as drug and material discovery. Recent approaches leverage diffu…
Graph GenerationKnowledge GraphsGraph LearningTransport-Coupled Bayesian Flows for Molecular Graph Generation
Molecular graph generation (MGG) is essentially a multi-class generative task, aimed at predicting categories of atoms and bonds under strict chemical and structural constraints. However, many prevailing diffusion paradi…
Graph GenerationEfficient Learning-based Graph Simulation for Temporal Graphs
Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving grap…
Graph GenerationPolyGraph Discrepancy: a classifier-based metric for graph generation
Existing methods for evaluating graph generative models primarily rely on Maximum Mean Discrepancy (MMD) metrics based on graph descriptors. While these metrics can rank generative models, they do not provide an absolute…
Graph GenerationToward a Unified Geometry Understanding: Riemannian Diffusion Framework for Graph Generation and Prediction
Graph diffusion models have made significant progress in learning structured graph data and have demonstrated strong potential for predictive tasks. Existing approaches typically embed node, edge, and graph-level feature…
Graph GenerationGraph-Aware Diffusion for Signal Generation
We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our approach builds on generative diffusion model…
Graph GenerationMulti-scale Autoregressive Models are Laplacian, Discrete, and Latent Diffusion Models in Disguise
We reinterpret Visual Autoregressive (VAR) models as iterative refinement models to identify which design choices drive their quality-efficiency trade-off. Instead of treating VAR only as next-scale autoregression, we fo…
Weather ForecastingGraph GenerationGraph Generation with Spectral Geodesic Flow Matching
Graph generation is a fundamental task with wide applications in modeling complex systems. Although existing methods align the spectrum or degree profile of the target graph, they often ignore the geometry induced by eig…
Graph GenerationVizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture
Data visualization is essential for interpreting complex datasets, yet traditional tools often require technical expertise, limiting accessibility. VizGen is an AI-assisted graph generation system that empowers users to …
Graph GenerationGraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing approaches are confined to single-graph,…
Graph GenerationGraph LearningGraphWeave: Interpretable and Robust Graph Generation via Random Walk Trajectories
Given a set of graphs from some unknown family, we want to generate new graphs from that family. Recent methods use diffusion on either graph embeddings or the discrete space of nodes and edges. However, simple changes t…
Graph GenerationFrom domain-landmark graph learning to problem-landmark graph generation
Landmarks have long played a pivotal role in automated planning, serving as crucial elements for improving the planning algorithms. The main limitation of classical landmark extraction methods is their sensitivity to spe…
Graph GenerationGraph LearningComposable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation
Controllable molecular graph generation is essential for material and drug discovery, where generated molecules must satisfy diverse property constraints. While recent advances in graph diffusion models have improved gen…
Graph GenerationDrug Discovery