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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 251–300 of 403 papers

TitleStatusHype
Aligning Users Across Social Networks Using Network Embedding—0
ALPINE: Active Link Prediction using Network Embedding—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
A multi-domain virtual network embedding algorithm with delay prediction—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
ASBERT: Siamese and Triplet network embedding for open question answering—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Associative Learning for Network Embedding—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Bib2vec: Embedding-based Search System for Bibliographic Information—0
Big Networks: A Survey—0
Broad Learning for Healthcare—0
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?—0
Hedging carbon risk with a network approach—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Collaborative filtering via heterogeneous neural networks—0
Community Aware Random Walk for Network Embedding—0
Community detection using low-dimensional network embedding algorithms—0
Complex Network Classification with Convolutional Neural Network—0
Compositional Network Embedding—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Deep Adversarial Network Alignment—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Deep Contrastive Multiview Network Embedding—0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
Deep Hashing for Signed Social Network Embedding—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Deep Learning for Learning Graph Representations—0
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