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 451–460 of 712 papers

TitleStatusHype
Constrained Structure Learning for Scene Graph Generation—0
ALF: Adaptive Label Finetuning for Scene Graph Generation—0
Contextual Associated Triplet Queries for Panoptic Scene Graph Generation—0
Continuous Graph Flow—0
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning—0
CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations—0
FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs—0
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization—0
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Benchmark Results

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