SOTAVerified

Graph Embedding

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Papers

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Showing 351–375 of 1192 papers

TitleStatusHype
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)—0
EventKE: Event-Enhanced Knowledge Graph Embedding—0
A Relational Tucker Decomposition for Multi-Relational Link Prediction—0
Graph Embedding Based Hybrid Social Recommendation System—0
A Comprehensive Survey on Community Detection with Deep Learning—0
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs—0
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis—0
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching—0
Expanding Semantic Knowledge for Zero-shot Graph Embedding—0
Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies—0
Graph Embeddings at Scale—0
Graph Embedding with Data Uncertainty—0
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
Neural IR Meets Graph Embedding: A Ranking Model for Product Search—0
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications—0
Enhancing Patent Retrieval using Text and Knowledge Graph Embeddings: A Technical Note—0
Exponential Family Graph Embeddings—0
Graph-based Predictable Feature Analysis—0
EXTRACT: Explainable Transparent Control of Bias in Embeddings—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds—0
Representing Hierarchical Structure by Using Cone Embedding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified