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 461–470 of 712 papers

TitleStatusHype
On Hierarchical Multi-Resolution Graph Generative Models—0
Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows—0
Multimodal Subtask Graph Generation from Instructional Videos—0
Unsupervised Task Graph Generation from Instructional Video Transcripts—0
From Graph Generation to Graph Classification—0
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
Self-Supervised Relation Alignment for Scene Graph Generation—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Improving Graph Generation by Restricting Graph BandwidthCode0
DDS: Decoupled Dynamic Scene-Graph Generation Network—0
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Benchmark Results

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