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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 211220 of 403 papers

TitleStatusHype
Temporal Network Representation Learning via Historical Neighborhoods AggregationCode0
RNE: A Scalable Network Embedding for Billion-scale Recommendation0
Unsupervised Graph Embedding via Adaptive Graph Learning0
EPINE: Enhanced Proximity Information Network Embedding0
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings0
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
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