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 381–390 of 403 papers

TitleStatusHype
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
Demographic Inference on Twitter using Recursive Neural Networks—0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
Attributed Network Embedding for Learning in a Dynamic Environment—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
struc2vec: Learning Node Representations from Structural IdentityCode0
Bib2vec: Embedding-based Search System for Bibliographic Information—0
Distributed Representations of Signed Networks—0
Font Size: Community Preserving Network EmbeddingCode0
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
Show:102550
← PrevPage 39 of 41Next →

No leaderboard results yet.