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 201–225 of 712 papers

TitleStatusHype
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning CapabilityCode1
Fast Graph Generation via Spectral DiffusionCode1
HYGENE: A Diffusion-based Hypergraph Generation MethodCode1
Scene Graph Generation with Role-Playing Large Language ModelsCode1
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Linguistic Structures as Weak Supervision for Visual Scene Graph GenerationCode1
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation—0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs—0
Automated Generation of Precedence Graphs in Digital Value Chains for Automotive Production—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
Deep imitation learning for molecular inverse problems—0
Deep Graph Generators: A Survey—0
AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
Graph Deconvolutional Generation—0
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation—0
Decomposed Prototype Learning for Few-Shot Scene Graph Generation—0
A Tunable Model for Graph Generation Using LSTM and Conditional VAE—0
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
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Benchmark Results

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