SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 241–250 of 403 papers

TitleStatusHype
Tag2Vec: Learning Tag Representations in Tag Networks—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
A Block-based Generative Model for Attributed Networks Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Adversarial Attacks on Deep Graph Matching—0
Adversarial Network Embedding—0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
A General Framework for Content-enhanced Network Representation Learning—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Show:102550
← PrevPage 25 of 41Next →

No leaderboard results yet.