Papers Graph Generation
“Graph Generation” 태그가 달린 논문 831편 · 필터 해제
VecMol: Vector-Field Representations for 3D Molecule Generation
Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing approaches typically represent molecules as 3D graphs and co-generate dis…
Graph GenerationDrug DiscoveryGraph-GRPO: Training Graph Flow Models with Reinforcement Learning
Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, \aka, graph flow model (GFM), has emerged due to its superior performance a…
Reinforcement LearningGraph GenerationDrug DiscoveryInstruction set for the representation of graphs
We present IsalGraph, a method for representing the structure of any finite, simple graph as a compact string over a nine-character instruction alphabet. The encoding is executed by a small virtual machine comprising a s…
Language ModellingGraph SimilarityGraph GenerationLocal Message-Passing for Discrete Graph Generation
Discrete graph generation has emerged as a powerful paradigm for modeling graph-structured data, yet state of the art models often rely on Graph Transformers or higher order architectures. We revisit this design assumpti…
Graph GenerationDM-CFO: A Diffusion Model for Compositional 3D Tooth Generation with Collision-Free Optimization
The automatic design of a 3D tooth model plays a crucial role in dental digitization. However, current approaches face challenges in compositional 3D tooth generation because both the layouts and shapes of missing teeth …
Graph Generation3D GenerationMitigating topology biases in Graph Diffusion via Counterfactual Intervention
Graph diffusion models have gained significant attention in graph generation tasks, but they often inherit and amplify topology biases from sensitive attributes (e.g. gender, age, region), leading to unfair synthetic gra…
Graph GenerationHow the Graph Construction Technique Shapes Performance in IoT Botnet Detection
The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms to model long-range feature dependencies …
Graph GenerationFlowette: Flow Matching with Graphette Priors for Graph Generation
We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transformer to learn a velocity field over graph …
Graph Neural NetworkGraph GenerationMolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely adopted due to the discrete nature of 2D m…
Graph GenerationDrug DiscoveryNeural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a n…
Graph GenerationRethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
Many generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equivariance through architectural constraint…
Graph GenerationStructural Bias Beyond Homophily: A Study of Fairness in Link Prediction
Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this task. While many fairness-aware methods …
Graph GenerationLink PredictionGraph LearningAstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning
Analog and mixed-signal (AMS) integrated circuits (ICs) lie at the core of modern computing and communications systems. However, despite the continued rise in design complexity, advances in AMS automation remain limited.…
Reinforcement LearningGraph GenerationPerformance Evaluation of LLMs in Automated RDF Knowledge Graph Generation
Cloud systems generate large, heterogeneous log data containing critical infrastructure, application, and security information. Transforming these logs into RDF triples enables their integration into knowledge graphs, im…
Few-Shot LearningGraph GenerationKnowledge GraphsAutoregressive Models for Knowledge Graph Generation
Knowledge Graph (KG) generation requires models to learn complex semantic dependencies between triples while maintaining domain validity constraints. Unlike link prediction, which scores triples independently, generative…
Computational EfficiencyGraph GenerationLink PredictionGenerator-based Graph Generation via Heat Diffusion
Graph generative modelling has become an essential task due to the wide range of applications in chemistry, biology, social networks, and knowledge representation. In this work, we propose a novel framework for generatin…
Graph GenerationUSS-Nav: Unified Spatio-Semantic Scene Graph for Lightweight UAV Zero-Shot Object Navigation
Zero-Shot Object Navigation in unknown environments poses significant challenges for Unmanned Aerial Vehicles (UAVs) due to the conflict between high-level semantic reasoning requirements and limited onboard computationa…
Computational EfficiencyGraph GenerationGraph ClusteringVariational Bayesian Flow Network for Graph Generation
Graph generation aims to sample discrete node and edge attributes while satisfying coupled structural constraints. Diffusion models for graphs often adopt largely factorized forward-noising, and many flow-matching method…
Graph GenerationAdaptive Edge Learning for Density-Aware Graph Generation
Generating realistic graph-structured data is challenging due to discrete structures, variable sizes, and class-specific connectivity patterns that resist conventional generative modelling. While recent graph generation …
Data AugmentationGraph GenerationGCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level
Deep graph learning models have demonstrated remarkable capabilities in processing graph-structured data and have been widely applied across various fields. However, their complex internal architectures and lack of trans…
Graph GenerationGraph Learning