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 221–230 of 712 papers

TitleStatusHype
DDS: Decoupled Dynamic Scene-Graph Generation Network—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Attentive Relational Networks for Mapping Images to Scene Graphs—0
Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment—0
GEMS: Scene Expansion using Generative Models of Graphs—0
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos—0
A Systematic Survey on Deep Generative Models for Graph Generation—0
A graph similarity for deep learning—0
G2A2: An Automated Graph Generator with Attributes and Anomalies—0
FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction—0
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Benchmark Results

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