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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 91–100 of 403 papers

TitleStatusHype
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
GENE: Global Event Network EmbeddingCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
Attributed Network Embedding via Subspace DiscoveryCode0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
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