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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 301–310 of 712 papers

Title	Date	Tasks	Status	Hype	Score
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation	Jul 18, 2021	Graph Generation	CodeCode Available	0	5
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning	May 8, 2024	Graph GenerationMissing Elements	CodeCode Available	0	5
Disentangled Dynamic Graph Deep Generation	Oct 14, 2020	Graph GenerationProtein Folding	CodeCode Available	0	5
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs	Jun 21, 2022	Anomaly DetectionBenchmarking	CodeCode Available	0	5
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement	Jun 9, 2020	DisentanglementGraph Generation	CodeCode Available	0	5
HBS -- Hardware Build System: A Tcl-based, minimal common abstraction approach for build system for hardware designs	Apr 13, 2025	Graph Generation	CodeCode Available	0	5
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation	Dec 11, 2024	BenchmarkingFederated Learning	CodeCode Available	0	5
A Diffusion Model for Event Skeleton Generation	May 27, 2023	DenoisingGraph Generation	CodeCode Available	0	5
Benchmark for Research Theme Classification of Scholarly Documents	Oct 1, 2022	ClassificationGraph Generation	CodeCode Available	0	5
Image-Conditioned Graph Generation for Road Network Extraction	Oct 31, 2019	Drug DesignGraph 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