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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

Showing 1120 of 403 papers

TitleStatusHype
SiReN: Sign-Aware Recommendation Using Graph Neural NetworksCode1
A Survey on Role-Oriented Network EmbeddingCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Robust Dynamic Network Embedding via EnsemblesCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Mutual Contrastive Learning for Visual Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional NetworksCode1
SDGNN: Learning Node Representation for Signed Directed NetworksCode1
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