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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 101–125 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
Scalable Hierarchical Embeddings of Complex Networks—0
Neurally boosted supervised spectral clustering—0
Multi-Vector Embedding on Networks with Taxonomies—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
Temporal Network Embedding via Tensor Factorization—0
Signed Bipartite Graph Neural NetworksCode1
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
SiReN: Sign-Aware Recommendation Using Graph Neural NetworksCode1
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
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