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 626–650 of 712 papers

TitleStatusHype
Exploring the Hierarchy in Relation Labels for Scene Graph Generation—0
Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models—0
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization—0
HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation—0
Assisting Scene Graph Generation with Self-Supervision—0
Structural Plan of Indoor Scenes with Personalized Preferences—0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition—0
A Systematic Survey on Deep Generative Models for Graph Generation—0
Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases—0
Scalable Deep Generative Modeling for Sparse Graphs—0
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"—0
MALOnt: An Ontology for Malware Threat IntelligenceCode0
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation—0
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral CollegeCode0
Interpretable Deep Graph Generation with Node-Edge Co-DisentanglementCode0
Graph-Aware Transformer: Is Attention All Graphs Need?—0
SHADOWCAST: Controllable Graph Generation—0
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative ModelsCode0
Visual Relationship Detection using Scene Graphs: A Survey—0
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations—0
R2RML and RML Comparison for RDF Generation, their Rules Validation and Inconsistency Resolution—0
TOMA: Topological Map Abstraction for Reinforcement Learning—0
Molecular Inverse-Design Platform for Material Industries—0
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets—0
Graph Deconvolutional Generation—0
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Benchmark Results

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