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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 311–320 of 712 papers

Title	Date	Tasks	Status	Hype	Score
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement	Jun 9, 2020	DisentanglementGraph Generation	CodeCode Available	0	5
Graph Edit Networks	Jan 1, 2021	AttributeGraph Generation	CodeCode Available	0	5
Input Conditioned Graph Generation for Language Agents	Jun 17, 2024	Graph GenerationMMLU	CodeCode Available	0	5
GraphEBM: Molecular Graph Generation with Energy-Based Models	Jan 31, 2021	Graph GenerationMolecular Graph Generation	CodeCode Available	0	5
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation	Dec 20, 2024	Anomaly DetectionData Augmentation	CodeCode Available	0	5
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model	Aug 19, 2024	Computational chemistryDenoising	CodeCode Available	0	5
Image-Conditioned Graph Generation for Road Network Extraction	Oct 31, 2019	Drug DesignGraph Generation	CodeCode Available	0	5
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation	Jun 7, 2018	Graph GenerationMolecular Graph Generation	CodeCode Available	0	5
Diffusion Twigs with Loop Guidance for Conditional Graph Generation	Oct 31, 2024	Graph Generation	CodeCode Available	0	5
Improving Graph Generation by Restricting Graph Bandwidth	Jan 25, 2023	Graph Generation	CodeCode Available	0	5

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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