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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 11–20 of 403 papers

TitleStatusHype
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction—0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
Simultaneous Weight and Architecture Optimization for Neural NetworksCode0
Simplifying complex machine learning by linearly separable network embedding spaces—0
Efficient Network Embedding by Approximate Equitable PartitionsCode0
A Survey on Signed Graph Embedding: Methods and Applications—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
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