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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 51–75 of 403 papers

TitleStatusHype
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
Deep Learning for Learning Graph Representations—0
Deep Hashing for Signed Social Network Embedding—0
Deep Partial Multiplex Network Embedding—0
Big Networks: A Survey—0
Bib2vec: Embedding-based Search System for Bibliographic Information—0
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
A Block-based Generative Model for Attributed Networks Embedding—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Adversarial Network Embedding—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—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
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
ALPINE: Active Link Prediction using Network Embedding—0
Adversarial Attacks on Deep Graph Matching—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
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