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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 61–70 of 403 papers

TitleStatusHype
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Deep Contrastive Multiview Network Embedding—0
Adversarial Network Embedding—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
A multi-domain virtual network embedding algorithm with delay prediction—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors—0
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