SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

Showing 676700 of 712 papers

TitleStatusHype
Counterfactual Critic Multi-Agent Training for Scene Graph Generation0
Learning to Compose Dynamic Tree Structures for Visual ContextsCode2
Attentive Relational Networks for Mapping Images to Scene Graphs0
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation0
An Interpretable Model for Scene Graph Generation0
Scene Graph Generation via Conditional Random Fields0
LinkNet: Relational Embedding for Scene GraphCode0
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks0
Encoding Robust Representation for Graph GenerationCode0
Graph Generation via Scattering0
Characterizing Malicious Edges targeting on Graph Neural Networks0
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic ParsingCode0
Aesthetic Discrimination of Graph LayoutsCode0
Graph R-CNN for Scene Graph GenerationCode1
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoningCode1
Natural Language Processing for Music Knowledge DiscoveryCode0
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph GenerationCode0
Graph Convolutional Policy Network for Goal-Directed Molecular Graph GenerationCode0
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint0
Approximate Query Matching for Image Retrieval0
Learning Deep Generative Models of Graphs0
NetGAN: Generating Graphs via Random WalksCode0
Growing Story Forest Online from Massive Breaking NewsCode0
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive ModelsCode0
Junction Tree Variational Autoencoder for Molecular Graph GenerationCode1
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Benchmark Results

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