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 221–230 of 712 papers

TitleStatusHype
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation—0
From Data to Modeling: Fully Open-vocabulary Scene Graph Generation—0
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical StudyCode0
GCAL: Adapting Graph Models to Evolving Domain ShiftsCode0
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph RetrievalCode0
ORQA: A Benchmark and Foundation Model for Holistic Operating Room Modeling—0
Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation—0
Automated Generation of Precedence Graphs in Digital Value Chains for Automotive Production—0
Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension—0
Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts—0
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Benchmark Results

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