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Network Embedding

Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 76–100 of 403 papers

TitleStatusHype
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
GENE: Global Event Network EmbeddingCode0
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News DetectionCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
CSNE: Conditional Signed Network EmbeddingCode0
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
Global Vectors for Node RepresentationsCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
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