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Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 621–630 of 712 papers

Title	Date	Tasks	Status	Hype
SHADOWCAST: Controllable Graph Generation	Jun 6, 2020	Generative Adversarial NetworkGraph Generation	—Unverified	0
XGNN: Towards Model-Level Explanations of Graph Neural Networks	Jun 3, 2020	Graph Generationvalid	CodeCode Available	1
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation	May 17, 2020	Graph GenerationScene Graph Generation	CodeCode Available	1
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models	May 17, 2020	AttributeGenerative Adversarial Network	CodeCode Available	0
Visual Relationship Detection using Scene Graphs: A Survey	May 16, 2020	Graph GenerationImage Generation	—Unverified	0
R2RML and RML Comparison for RDF Generation, their Rules Validation and Inconsistency Resolution	May 13, 2020	Graph GenerationKnowledge Graphs	—Unverified	0
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations	May 13, 2020	Graph Generation	—Unverified	0
TOMA: Topological Map Abstraction for Reinforcement Learning	May 11, 2020	Graph Generationreinforcement-learning	—Unverified	0
Secure Deep Graph Generation with Link Differential Privacy	May 1, 2020	Graph GenerationLink Prediction	CodeCode Available	1
Molecular Inverse-Design Platform for Material Industries	Apr 24, 2020	Graph GenerationMolecular Graph Generation	—Unverified	0

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	RNN	StreetMover	0.03	—	Unverified
2	GraphRNN	StreetMover	0.02	—	Unverified
3	GGT without CA	StreetMover	0.02	—	Unverified
4	GGT	StreetMover	0.02	—	Unverified