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 611–620 of 712 papers

TitleStatusHype
GPT-GNN: Generative Pre-Training of Graph Neural NetworksCode1
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"—0
MALOnt: An Ontology for Malware Threat IntelligenceCode0
MoFlow: An Invertible Flow Model for Generating Molecular GraphsCode1
Learning Visual Commonsense for Robust Scene Graph GenerationCode1
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
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
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Benchmark Results

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