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 641–650 of 712 papers

TitleStatusHype
Edge-based sequential graph generation with recurrent neural networksCode0
GraphAF: a Flow-based Autoregressive Model for Molecular Graph GenerationCode1
Graph Generators: State of the Art and Open Challenges—0
GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph GenerationCode1
NODIS: Neural Ordinary Differential Scene UnderstandingCode1
Weakly Supervised Visual Semantic ParsingCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Deep imitation learning for molecular inverse problems—0
Effective Decoding in Graph Auto-Encoder using Triadic Closure—0
Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction—0
Show:102550
← PrevPage 65 of 72Next →

Benchmark Results

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