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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 231–240 of 403 papers

TitleStatusHype
Source-Aware Embedding Training on Heterogeneous Information Networks—0
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method—0
Space-Invariant Projection in Streaming Network Embedding—0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
Stationary distribution of node2vec random walks on household models—0
Streaming Network Embedding through Local Actions—0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding—0
Subgraph Networks with Application to Structural Feature Space Expansion—0
Subset-Contrastive Multi-Omics Network Embedding—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
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