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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 121–130 of 403 papers

TitleStatusHype
A Block-based Generative Model for Attributed Networks Embedding—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Graph-Level Embedding for Time-Evolving Graphs—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Equivalence between LINE and Matrix Factorization—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
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