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 521–530 of 712 papers

TitleStatusHype
Importance Weighted Structure Learning for Scene Graph Generation—0
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph—0
Supplementing Missing Visions via Dialog for Scene Graph GenerationsCode0
Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph—0
Synthetic Graph Generation to Benchmark Graph Learning—0
Gransformer: Transformer-based Graph Generation—0
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation—0
A Survey on Deep Graph Generation: Methods and Applications—0
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph GeneratorsCode0
Dual Embodied-Symbolic Concept Representations for Deep Learning—0
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Benchmark Results

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