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 311–320 of 712 papers

TitleStatusHype
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint—0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Characterizing Malicious Edges targeting on Graph Neural Networks—0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding—0
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks—0
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation—0
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Benchmark Results

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