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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 341350 of 403 papers

TitleStatusHype
Neural Embedding Propagation on Heterogeneous NetworksCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingCode0
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert FindingCode0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author IdentificationCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Binarized Attributed Network EmbeddingCode0
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