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 551–560 of 712 papers

TitleStatusHype
Scene Graph Generation with Geometric Context—0
Network Generation with Differential Privacy—0
Improving Query Graph Generation for Complex Question Answering over Knowledge Base—0
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labellingCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Molecular Graph Generation via Geometric Scattering—0
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation—0
Top-N: Equivariant set and graph generation without exchangeabilityCode0
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer—0
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation—0
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Benchmark Results

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