SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 271–280 of 712 papers

TitleStatusHype
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models—0
SceneLLM: Implicit Language Reasoning in LLM for Dynamic Scene Graph Generation—0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
Efficient Dynamic Attributed Graph Generation—0
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph GenerationCode0
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation—0
HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing—0
Graph Community Augmentation with GMM-based Modeling in Latent Space—0
Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation—0
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Benchmark Results

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