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 681–690 of 712 papers

TitleStatusHype
Counterfactual Critic Multi-Agent Training for Scene Graph Generation—0
Attentive Relational Networks for Mapping Images to Scene Graphs—0
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation—0
An Interpretable Model for Scene Graph Generation—0
Scene Graph Generation via Conditional Random Fields—0
LinkNet: Relational Embedding for Scene GraphCode0
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks—0
Encoding Robust Representation for Graph GenerationCode0
Graph Generation via Scattering—0
Characterizing Malicious Edges targeting on Graph Neural Networks—0
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Benchmark Results

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