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 371–380 of 712 papers

TitleStatusHype
Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction—0
Efficient Dynamic Attributed Graph Generation—0
Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation—0
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering—0
Ensemble Predicate Decoding for Unbiased Scene Graph Generation—0
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding—0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development—0
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
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Benchmark Results

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