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 261–270 of 403 papers

TitleStatusHype
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
ASBERT: Siamese and Triplet network embedding for open question answering—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Associative Learning for Network Embedding—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
Show:102550
← PrevPage 27 of 41Next →

No leaderboard results yet.