SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

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Showing 271–280 of 712 papers

TitleStatusHype
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical StudyCode0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural NetworkCode0
Edge-based sequential graph generation with recurrent neural networksCode0
An Equivariant Generative Framework for Molecular Graph-Structure Co-DesignCode0
Balanced Graph Structure Learning for Multivariate Time Series ForecastingCode0
KnowZRel: Common Sense Knowledge-based Zero-Shot Relationship Retrieval for Generalised Scene Graph GenerationCode0
DSGG: Dense Relation Transformer for an End-to-end Scene Graph GenerationCode0
Interpretable Deep Graph Generation with Node-Edge Co-DisentanglementCode0
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion ModelCode0
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Benchmark Results

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