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 351–400 of 712 papers

TitleStatusHype
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation—0
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation—0
DIG: A Turnkey Library for Diving into Graph Deep Learning Research—0
Discovering Graph Generation Algorithms—0
Disentangled Spatiotemporal Graph Generative Models—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Distributed Training of Embeddings using Graph Analytics—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?—0
Domain-wise Invariant Learning for Panoptic Scene Graph Generation—0
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation—0
Dual Embodied-Symbolic Concept Representations for Deep Learning—0
Dual ResGCN for Balanced Scene GraphGeneration—0
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams—0
Dynamic Relation Transformer for Contextual Text Block Detection—0
Dynamic Scene Graph Generation via Anticipatory Pre-Training—0
EDGE++: Improved Training and Sampling of EDGE—0
EDoG: Adversarial Edge Detection For Graph Neural Networks—0
Effective Decoding in Graph Auto-Encoder using Triadic Closure—0
Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction—0
Efficient Dynamic Attributed Graph Generation—0
Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation—0
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering—0
Ensemble Predicate Decoding for Unbiased Scene Graph Generation—0
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding—0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development—0
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
Explore Contextual Information for 3D Scene Graph Generation—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Exploring the Hierarchy in Relation Labels for Scene Graph Generation—0
Exploring the Potential of Large Language Models in Graph Generation—0
Target-Tailored Source-Transformation for Scene Graph Generation—0
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization—0
FairGen: Towards Fair Graph Generation—0
Fairness Amidst Non-IID Graph Data: A Literature Review—0
FairWire: Fair Graph Generation—0
Fast Contextual Scene Graph Generation With Unbiased Context Augmentation—0
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search—0
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators—0
FLOWGEN: Fast and slow graph generation—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding—0
From Data to Modeling: Fully Open-vocabulary 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