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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

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Showing 321–330 of 403 papers

TitleStatusHype
Initialization for Network Embedding: A Graph Partition Approach—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Efficient Training on Very Large Corpora via Gramian Estimation—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
Embedding Node Structural Role Identity into Hyperbolic Space—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—0
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
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