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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 331340 of 403 papers

TitleStatusHype
SCE: Scalable Network Embedding from Sparsest CutCode0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
Search Efficient Binary Network EmbeddingCode0
Network Representation Learning with Rich Text InformationCode0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky SocialCode0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
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