SOTAVerified

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

Showing 121130 of 403 papers

TitleStatusHype
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
Efficient Network Embedding by Approximate Equitable PartitionsCode0
Global Vectors for Node RepresentationsCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
Flexible Attributed Network EmbeddingCode0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
Show:102550
← PrevPage 13 of 41Next →

No leaderboard results yet.