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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 151–175 of 403 papers

TitleStatusHype
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
Temporal Network Embedding via Tensor Factorization—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
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
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
GENE: Global Event Network EmbeddingCode0
Network embedding unveils the hidden interactions in the mammalian virome—0
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
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks—0
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
Hierarchical Graph Neural Networks—0
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