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 401–425 of 712 papers

TitleStatusHype
Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
A graph similarity for deep learning—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
A Modern Take on Visual Relationship Reasoning for Grasp Planning—0
An Accurate Graph Generative Model with Tunable Features—0
AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models—0
Analyzing Hong Kong's Legal Judgments from a Computational Linguistics point-of-view—0
An Automated Domain Understanding Technique for Knowledge Graph Generation—0
An End-to-End Network for Generating Social Relationship Graphs—0
An Enhanced Object Detection Model for Scene Graph Generation—0
An Interpretable Model for Scene Graph Generation—0
A Novel Weighted Distance Measure for Multi-Attributed Graph—0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model—0
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint—0
Approximate Query Matching for Image Retrieval—0
A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
Assisting Scene Graph Generation with Self-Supervision—0
A Survey on Deep Graph Generation: Methods and Applications—0
A Systematic Survey on Deep Generative Models for Graph Generation—0
Attentive Relational Networks for Mapping Images to Scene Graphs—0
A Tunable Model for Graph Generation Using LSTM and Conditional VAE—0
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Benchmark Results

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