SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

Showing 551575 of 712 papers

TitleStatusHype
Energy-Based Learning for Scene Graph GenerationCode1
Graph Energy-based Model for Molecular Graph Generation0
Pre-Training on Dynamic Graph Neural NetworksCode0
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Malware Knowledge Graph Generation0
In-game Residential Home Planning via Visual Context-aware Global Relation Learning0
GraphDF: A Discrete Flow Model for Molecular Graph Generation0
GraphEBM: Molecular Graph Generation with Energy-Based ModelsCode0
Understanding the Role of Scene Graphs in Visual Question Answering0
A Simple Baseline for Weakly-Supervised Scene Graph Generation0
Topic Scene Graph Generation by Attention Distillation From Caption0
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
Learning Latent Topology for Graph Matching0
Graph Edit NetworksCode0
Counterfactual Thinking for Long-tailed Information Extraction0
GG-GAN: A Geometric Graph Generative Adversarial Network0
An Automated Domain Understanding Technique for Knowledge Graph Generation0
Deep Graph Generators: A Survey0
MG-SAGC: A multiscale graph and its self-adaptive graph convolution network for 3D point clouds0
Graph Autoencoders with Deconvolutional Networks0
Molecular graph generation with Graph Neural Networks0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs0
A graph similarity for deep learning0
Self-Supervised Real-to-Sim Scene Generation0
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Benchmark Results

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