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 201–210 of 712 papers

TitleStatusHype
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Knowledge-Embedded Routing Network for Scene Graph GenerationCode1
Graph R-CNN for Scene Graph GenerationCode1
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoningCode1
Junction Tree Variational Autoencoder for Molecular Graph GenerationCode1
Scene Graph Generation by Iterative Message PassingCode1
NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation—0
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text RepresentationCode0
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning—0
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Benchmark Results

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