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 161–170 of 712 papers

TitleStatusHype
Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential EquationsCode1
Semantic Similarity Score for Measuring Visual Similarity at Semantic Level—0
Leveraging Predicate and Triplet Learning for Scene Graph GenerationCode1
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion ModelsCode0
OED: Towards One-stage End-to-End Dynamic Scene Graph GenerationCode1
Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms—0
REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs in Scene Graph GenerationCode2
Score-based Generative Models with Adaptive Momentum—0
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Benchmark Results

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