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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 51–100 of 403 papers

TitleStatusHype
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Embedding Node Structural Role Identity into Hyperbolic Space—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
Efficient Training on Very Large Corpora via Gramian Estimation—0
Adversarial Network Embedding—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated 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
Initialization for Network Embedding: A Graph Partition Approach—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
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
dynnode2vec: Scalable Dynamic Network Embedding—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering—0
Aligning context-based statistical models of language with brain activity during reading—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Dynamic Graph Embedding via LSTM History Tracking—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Deep Adversarial Network Alignment—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Deep Contrastive Multiview Network Embedding—0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—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
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Deep Learning for Learning Graph Representations—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Aligning Users Across Social Networks Using Network Embedding—0
Deep Partial Multiplex Network Embedding—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
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