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 81–90 of 712 papers

TitleStatusHype
Energy-Based Learning for Scene Graph GenerationCode1
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph GenerationCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive LearningCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Diffusion-based Graph Generative MethodsCode1
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph EngineeringCode1
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Benchmark Results

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