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 401–425 of 712 papers

TitleStatusHype
Gradient-Guided Importance Sampling for Learning Binary Energy-Based ModelsCode0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
Unbiased Scene Graph Generation using Predicate Similarities—0
Benchmark for Research Theme Classification of Scholarly DocumentsCode0
Overview of the Third Workshop on Scholarly Document Processing—0
DiGress: Discrete Denoising diffusion for graph generationCode2
MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction—0
Face Super-Resolution Using Stochastic Differential EquationsCode1
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity—0
Scalable Regularization of Scene Graph Generation Models using Symbolic Theories—0
Influence Maximization (IM) in Complex Networks with Limited Visibility Using Statistical Methods—0
Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning—0
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation—0
Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation—0
Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph GenerationCode1
Rethinking the Evaluation of Unbiased Scene Graph Generation—0
Iterative Scene Graph Generation—0
NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation—0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model—0
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation—0
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
FLOWGEN: Fast and slow graph generation—0
Unified 2D and 3D Pre-Training of Molecular RepresentationsCode1
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
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Benchmark Results

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