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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 101–150 of 403 papers

TitleStatusHype
Vaccine skepticism detection by network embeddingCode0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Multi-Relation Aware Temporal Interaction Network EmbeddingCode0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Latent Network Embedding via Adversarial Auto-encoders—0
Multi-Vector Embedding on Networks with Taxonomies—0
Neurally boosted supervised spectral clustering—0
Scalable Hierarchical Embeddings of Complex Networks—0
Network representation learning systematic review: ancestors and current development state—0
QUINT: Node embedding using network hashing—0
REFINE: Random RangE FInder for Network Embedding—0
Signed Bipartite Graph Neural NetworksCode1
Temporal Network Embedding via Tensor Factorization—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
SiReN: Sign-Aware Recommendation Using Graph Neural NetworksCode1
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
Deep Contrastive Multiview Network Embedding—0
TextCNN with Attention for Text ClassificationCode0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
A Survey on Role-Oriented Network EmbeddingCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for FontsCode0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Large-Scale Network Embedding in Apache Spark—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
Relation order histograms as a network embedding toolCode0
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
GENE: Global Event Network EmbeddingCode0
Network embedding unveils the hidden interactions in the mammalian virome—0
Robust Dynamic Network Embedding via EnsemblesCode1
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender SystemsCode0
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View—0
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Hierarchical Graph Neural Networks—0
MUSE: Multi-faceted Attention for Signed Network Embedding—0
Network Embedding via Deep Prediction Model—0
Mutual Contrastive Learning for Visual Representation LearningCode1
ASBERT: Siamese and Triplet network embedding for open question answering—0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
Dynamic Network Embedding Survey—0
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph EmbeddingCode0
Progresses and Challenges in Link Prediction—0
Fast Graph Learning with Unique Optimal SolutionsCode1
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