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 511–520 of 712 papers

TitleStatusHype
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
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
From Graph Generation to Graph Classification—0
Show:102550
← PrevPage 52 of 72Next →

Benchmark Results

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