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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 1–10 of 403 papers

TitleStatusHype
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Network Alignment—0
Subset-Contrastive Multi-Omics Network Embedding—0
Network Embedding Exploration Tool (NEExT)Code0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding—0
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees—0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Stationary distribution of node2vec random walks on household models—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—0
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