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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 321–330 of 403 papers

TitleStatusHype
RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network EmbeddingCode0
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity PreservedCode0
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vecCode0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Network Embedding Exploration Tool (NEExT)Code0
Network Embedding: on Compression and LearningCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Efficient Network Embedding by Approximate Equitable PartitionsCode0
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