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 421–430 of 712 papers

TitleStatusHype
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation—0
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
FLOWGEN: Fast and slow graph generation—0
Unified 2D and 3D Pre-Training of Molecular RepresentationsCode1
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Graph Generative Model for Benchmarking Graph Neural NetworksCode1
GEMS: Scene Expansion using Generative Models of Graphs—0
Unsupervised Knowledge Graph Generation Using Semantic Similarity MatchingCode0
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning—0
Learning To Generate Scene Graph from Head to Tail—0
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Benchmark Results

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