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 391–400 of 712 papers

TitleStatusHype
FairWire: Fair Graph Generation—0
Fast Contextual Scene Graph Generation With Unbiased Context Augmentation—0
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search—0
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators—0
FLOWGEN: Fast and slow graph generation—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
From Data to Modeling: Fully Open-vocabulary Scene Graph Generation—0
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Benchmark Results

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