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 571–580 of 712 papers

TitleStatusHype
Graph Autoencoders with Deconvolutional Networks—0
Molecular graph generation with Graph Neural Networks—0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs—0
A graph similarity for deep learning—0
Self-Supervised Real-to-Sim Scene Generation—0
Efficient Initial Pose-graph Generation for Global SfMCode1
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
Reinforced Molecular Optimization with Neighborhood-Controlled GrammarsCode0
After All, Only The Last Neuron Matters: Comparing Multi-modal Fusion Functions for Scene Graph GenerationCode0
Dual ResGCN for Balanced Scene GraphGeneration—0
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Benchmark Results

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