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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 281–290 of 403 papers

TitleStatusHype
NetSMF: Large-Scale Network Embedding as Sparse Matrix FactorizationCode0
Dynamic Network Embeddings for Network Evolution Analysis—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Task-Guided Pair Embedding in Heterogeneous NetworkCode0
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