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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 Node Structural Role Identity into Hyperbolic Space—0
Efficient Training on Very Large Corpora via Gramian Estimation—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—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
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
Adversarial Network Embedding—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering—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
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—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
Initialization for Network Embedding: A Graph Partition Approach—0
Efficient Inner Product Approximation in Hybrid Spaces—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—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 Network Embeddings for Network Evolution Analysis—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 Embedding Survey—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
Document Network Embedding: Coping for Missing Content and Missing Links—0
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