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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 301310 of 403 papers

TitleStatusHype
mvn2vec: Preservation and Collaboration in Multi-View Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
GENE: Global Event Network EmbeddingCode0
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Global Vectors for Node RepresentationsCode0
Enhanced Network Embedding with Text InformationCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
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