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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 1026–1050 of 1192 papers

TitleStatusHype
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph ProximityCode0
PyTorch-BigGraph: A Large-scale Graph Embedding SystemCode0
Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction PerspectiveCode1
Unsupervised Multi-modal Hashing for Cross-modal retrieval—0
Domain Representation for Knowledge Graph Embedding—0
Splitter: Learning Node Representations that Capture Multiple Social ContextsCode0
Deep Patent Landscaping Model Using Transformer and Graph Embedding—0
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs—0
Logic Rules Powered Knowledge Graph Embedding—0
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few LabelsCode0
Representation Learning with Weighted Inner Product for Universal Approximation of General SimilaritiesCode0
Deep Adversarial Network Alignment—0
EL Embeddings: Geometric construction of models for the Description Logic EL ++Code0
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex SpaceCode1
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference—0
Robust Graph Embedding with Noisy Link Weights—0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor FactorizationCode0
A Relational Tucker Decomposition for Multi-Relational Link Prediction—0
Neural IR Meets Graph Embedding: A Ranking Model for Product Search—0
Multi-Task Feature Learning for Knowledge Graph Enhanced RecommendationCode0
All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified