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 251–300 of 712 papers

TitleStatusHype
Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection—0
Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network—0
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL TranslationCode0
FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing—0
EDGE++: Improved Training and Sampling of EDGE—0
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?Code1
VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools—0
TextPSG: Panoptic Scene Graph Generation from Textual Descriptions—0
Domain-wise Invariant Learning for Panoptic Scene Graph Generation—0
Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationCode0
Less is More: Toward Zero-Shot Local Scene Graph Generation via Foundation ModelsCode1
Logical Bias Learning for Object Relation Prediction—0
Streamlining Attack Tree Generation: A Fragment-Based Approach—0
SANGEA: Scalable and Attributed Network Generation—0
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step RetrosynthesisCode1
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph GenerationCode1
Predicate Classification Using Optimal Transport Loss in Scene Graph Generation—0
Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention—0
STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation—0
Zero-Shot Scene Graph Generation via Triplet Calibration and ReductionCode1
RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation—0
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate ClassesCode0
An Accurate Graph Generative Model with Tunable Features—0
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