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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 151160 of 403 papers

TitleStatusHype
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
Global Vectors for Node RepresentationsCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
GENE: Global Event Network EmbeddingCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
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