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 241–250 of 712 papers

TitleStatusHype
From Route Instructions to Landmark Graphs—0
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization—0
A Survey on Deep Graph Generation: Methods and Applications—0
GG-GAN: A Geometric Graph Generative Adversarial Network—0
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation—0
Building Transportation Foundation Model via Generative Graph Transformer—0
GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs—0
From Graph Generation to Graph Classification—0
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs—0
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
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Benchmark Results

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