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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 131140 of 403 papers

TitleStatusHype
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
Enhanced Network Embedding with Text InformationCode0
Flexible Attributed Network EmbeddingCode0
Font Size: Community Preserving Network EmbeddingCode0
GENE: Global Event Network EmbeddingCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
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