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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

Showing 61–70 of 403 papers

TitleStatusHype
Global Vectors for Node RepresentationsCode0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Font Size: Community Preserving Network EmbeddingCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
GENE: Global Event Network EmbeddingCode0
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