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 391–400 of 712 papers

TitleStatusHype
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—0
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment—0
Accurate polyglot semantic parsing with DAG grammars—0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
Adversarial Stein Training for Graph Energy Models—0
Adversarial Stein Training for Graph Energy Models—0
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Benchmark Results

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