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 231–240 of 712 papers

TitleStatusHype
Optimized Crystallographic Graph Generation for Material ScienceCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
Pre-Training on Dynamic Graph Neural NetworksCode0
Connector 0.5: A unified framework for graph representation learningCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense ReasoningCode0
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical StudyCode0
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph GenerationCode0
LinkNet: Relational Embedding for Scene GraphCode0
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Benchmark Results

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