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 201–250 of 712 papers

TitleStatusHype
Sparse Training of Discrete Diffusion Models for Graph GenerationCode1
Fast Graph Generation via Spectral DiffusionCode1
Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain GraphCode1
Multiresolution Equivariant Graph Variational AutoencoderCode1
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
GPS-Net: Graph Property Sensing Network for Scene Graph GenerationCode1
S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in ORCode0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based ModelsCode0
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific NarrativesCode0
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion ModelsCode0
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text RepresentationCode0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Replica Tree-based Federated Learning using Limited DataCode0
Reinforced Molecular Optimization with Neighborhood-Controlled GrammarsCode0
ReFormer: The Relational Transformer for Image CaptioningCode0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph GeneratorsCode0
Recovering Barabási-Albert Parameters of Graphs through DisentanglementCode0
GCAL: Adapting Graph Models to Evolving Domain ShiftsCode0
Pre-Training on Dynamic Graph Neural NetworksCode0
Pixels to Graphs by Associative EmbeddingCode0
Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationCode0
Pixel-wise Graph Attention Networks for Person Re-identificationCode0
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral CollegeCode0
On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpediaCode0
Optimized Crystallographic Graph Generation for Material ScienceCode0
Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain MappingCode0
Node Embedding via Word Embedding for Network Community DiscoveryCode0
After All, Only The Last Neuron Matters: Comparing Multi-modal Fusion Functions for Scene Graph GenerationCode0
Fine-Grained Scene Graph Generation via Sample-Level Bias PredictionCode0
OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence RoboticsCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted NarrativesCode0
Natural Language Processing for Music Knowledge DiscoveryCode0
Connector 0.5: A unified framework for graph representation learningCode0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
NetGAN: Generating Graphs via Random WalksCode0
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph GenerationCode0
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense ReasoningCode0
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labellingCode0
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
LinkNet: Relational Embedding for Scene GraphCode0
Exploiting Long-Term Dependencies for Generating Dynamic Scene GraphsCode0
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical StudyCode0
Explanation Graph Generation via Generative Pre-training over Synthetic GraphsCode0
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Benchmark Results

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