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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 381–390 of 403 papers

TitleStatusHype
Learning a Deep Part-based Representation by Preserving Data Distribution—0
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network—0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
Learning Document Embeddings With CNNs—0
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks—0
Learning Large-scale Network Embedding from Representative Subgraph—0
MANELA: A Multi-Agent Algorithm for Learning Network Embeddings—0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding—0
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction—0
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