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
Energy-Based Learning for Scene Graph GenerationCode1
Graph Energy-based Model for Molecular Graph Generation—0
Pre-Training on Dynamic Graph Neural NetworksCode0
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Malware Knowledge Graph Generation—0
In-game Residential Home Planning via Visual Context-aware Global Relation Learning—0
GraphDF: A Discrete Flow Model for Molecular Graph Generation—0
GraphEBM: Molecular Graph Generation with Energy-Based ModelsCode0
Understanding the Role of Scene Graphs in Visual Question Answering—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
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Benchmark Results

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