SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 712 papers

TitleStatusHype
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Pixel-wise Graph Attention Networks for Person Re-identificationCode0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
Open-Vocabulary Object Detection via Scene Graph Discovery—0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction—0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization—0
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation—0
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing—0
Size Matters: Large Graph Generation with HiGGs—0
Using Motif Transitions for Temporal Graph GenerationCode0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
On Certified Generalization in Structured Prediction—0
Discrete Graph Auto-Encoder—0
Single-Stage Visual Relationship Learning using Conditional Queries—0
Optimized Crystallographic Graph Generation for Material ScienceCode0
LIC-GAN: Language Information Conditioned Graph Generative GAN Model—0
Explanation Graph Generation via Generative Pre-training over Synthetic GraphsCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
A Diffusion Model for Event Skeleton GenerationCode0
Building Transportation Foundation Model via Generative Graph Transformer—0
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03—Unverified
2GraphRNNStreetMover0.02—Unverified
3GGT without CAStreetMover0.02—Unverified
4GGTStreetMover0.02—Unverified