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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 5160 of 403 papers

TitleStatusHype
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Flexible Attributed Network EmbeddingCode0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
Font Size: Community Preserving Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
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