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 301–325 of 712 papers

TitleStatusHype
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Explore Contextual Information for 3D Scene Graph Generation—0
Collect and Connect Data Leaves to Feature Concepts: Interactive Graph Generation Toward Well-being—0
Approximate Query Matching for Image Retrieval—0
Interpreting Equivariant Representations—0
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
Clustering based opcode graph generation for malware variant detection—0
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
Is 3-(F)WL Enough to Distinguish All 3D Graphs?—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
Ensemble Predicate Decoding for Unbiased Scene Graph Generation—0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model—0
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering—0
Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation—0
CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery—0
Adversarial Stein Training for Graph Energy Models—0
Adversarial Stein Training for Graph Energy Models—0
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Benchmark Results

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