SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 441–450 of 712 papers

TitleStatusHype
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations—0
Graphs Unveiled: Graph Neural Networks and Graph Generation—0
GUISE: Graph GaUssIan Shading watErmark—0
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos—0
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps—0
Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling—0
Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments—0
Size Matters: Large Graph Generation with HiGGs—0
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation—0
On Hierarchical Multi-Resolution Graph Generative Models—0
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Benchmark Results

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