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 521–530 of 712 papers

TitleStatusHype
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Score-based Generative Models with Adaptive Momentum—0
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation—0
Secure Network Release with Link Privacy—0
Segmentation-grounded Scene Graph Generation—0
Self-Supervised Relation Alignment for Scene Graph Generation—0
Semantic Compositional Learning for Low-shot Scene Graph Generation—0
Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network—0
Semantic Similarity Score for Measuring Visual Similarity at Semantic Level—0
Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph—0
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Benchmark Results

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