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 251–260 of 712 papers

TitleStatusHype
From Graph Generation to Graph Classification—0
Gransformer: Transformer-based Graph Generation—0
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs—0
Graph Autoencoders with Deconvolutional Networks—0
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Assisting Scene Graph Generation with Self-Supervision—0
From Data to Modeling: Fully Open-vocabulary Scene Graph Generation—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing—0
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Benchmark Results

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