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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 691–700 of 712 papers

Title	Date	Tasks	Status	Hype
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models	May 17, 2020	AttributeGenerative Adversarial Network	CodeCode Available	0
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models	Oct 11, 2022	Graph Generation	CodeCode Available	0
TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning	Aug 30, 2019	Few-Shot LearningGraph Attention	CodeCode Available	0
Aesthetic Discrimination of Graph Layouts	Sep 4, 2018	Data AugmentationGraph Generation	CodeCode Available	0
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models	Sep 29, 2021	Graph Generation	CodeCode Available	0
A Diffusion Model for Event Skeleton Generation	May 27, 2023	DenoisingGraph Generation	CodeCode Available	0
Disentangled Dynamic Graph Deep Generation	Oct 14, 2020	Graph GenerationProtein Folding	CodeCode Available	0
Diffusion Twigs with Loop Guidance for Conditional Graph Generation	Oct 31, 2024	Graph Generation	CodeCode Available	0
The Limited Multi-Label Projection Layer	Jun 20, 2019	General ClassificationGraph Generation	CodeCode Available	0
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives	Dec 7, 2023	Graph GenerationLanguage Modelling	CodeCode Available	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