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 276–300 of 712 papers

TitleStatusHype
Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic GroundingCode0
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
Head-Tail Cooperative Learning Network for Unbiased Scene Graph GenerationCode0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
Vision Relation Transformer for Unbiased Scene Graph GenerationCode1
RLIPv2: Fast Scaling of Relational Language-Image Pre-trainingCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs—0
Informative Scene Graph Generation via Debiasing—0
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation—0
Generalized Unbiased Scene Graph Generation—0
Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph GenerationCode0
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from TextCode0
Improving Scene Graph Generation with Superpixel-Based Interaction Learning—0
Interpretable End-to-End Driving Model for Implicit Scene Understanding—0
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation—0
Panoptic Scene Graph Generation with Semantics-Prototype LearningCode1
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Pixel-wise Graph Attention Networks for Person Re-identificationCode0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Autoregressive Diffusion Model for Graph GenerationCode1
Pair then Relation: Pair-Net for Panoptic Scene Graph GenerationCode1
IntelliGraphs: Datasets for Benchmarking Knowledge Graph GenerationCode1
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
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Benchmark Results

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