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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 291300 of 403 papers

TitleStatusHype
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Flexible Attributed Network EmbeddingCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Font Size: Community Preserving Network EmbeddingCode0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Representation Learning on Heterostructures via Heterogeneous Anonymous WalksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Unsupervised Attributed Multiplex Network EmbeddingCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
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