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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 101110 of 403 papers

TitleStatusHype
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
Learning Role-based Graph EmbeddingsCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
Flexible Attributed Network EmbeddingCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Font Size: Community Preserving Network EmbeddingCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
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