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 581–590 of 712 papers

TitleStatusHype
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development—0
Accurate polyglot semantic parsing with DAG grammars—0
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit—0
Neural Language Modeling for Contextualized Temporal Graph GenerationCode1
Disentangled Dynamic Graph Deep GenerationCode0
Dirichlet Graph Variational AutoencoderCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
Scene Graph Modification Based on Natural Language CommandsCode0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Secure Network Release with Link Privacy—0
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Benchmark Results

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