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 161–170 of 403 papers

TitleStatusHype
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for FontsCode0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Large-Scale Network Embedding in Apache Spark—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
Relation order histograms as a network embedding toolCode0
GENE: Global Event Network EmbeddingCode0
Network embedding unveils the hidden interactions in the mammalian virome—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Show:102550
← PrevPage 17 of 41Next →

No leaderboard results yet.