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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

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Showing 371–380 of 403 papers

TitleStatusHype
Representation Learning for Scale-free Networks—0
Adversarial Network Embedding—0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Vertex-Context Sampling for Weighted Network Embedding—0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
Community Aware Random Walk for Network Embedding—0
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vecCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Equivalence between LINE and Matrix Factorization—0
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