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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 201210 of 403 papers

TitleStatusHype
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks0
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models0
PPPNE: Personalized proximity preserved network embedding0
Predict Anchor Links across Social Networks via an Embedding Approach0
Privacy Attacks on Network Embeddings0
Progresses and Challenges in Link Prediction0
QUINT: Node embedding using network hashing0
Random Walks: A Review of Algorithms and Applications0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement0
Recommending on graphs: a comprehensive review from a data perspective0
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